Tesis: "Spike detector"

1

Ervin, Brian. "Neural Spike Detection and Classification Using Massively Parallel Graphics Processing". University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868773.

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2

Carey, Howard J. III. "EEG Interictal Spike Detection Using Artificial Neural Networks". VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4648.

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Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce the amount of data for a neurologist to manually analyze. The effectiveness of multiple neural network implementations is compared, and a data reduction of 3-4 orders of magnitude, or upwards of 99%, is achieved.

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3

Malvestio, Irene. "Detection of directional interactions between neurons from spike trains". Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666226.

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An important problem in neuroscience is the assessment of the connectivity between neurons from their spike trains. One recent approach developed for the detection of directional couplings between dynamics based on recorded point processes is the nonlinear interdependence measure L. In this thesis we first use the Hindmarsh-Rose model system to test L in the presence of noise and for different spiking regimes of the dynamics. We then compare the performance of L against the linear cross-correlogram and two spike train distances. Finally, we apply all measures to neuronal spiking data from an intracranial whole-night recording of a patient with epilepsy. When applied to simulated data, L proves to be versatile, robust and more sensitive than the linear measures. Instead, in the real data the linear measures find more connections than L, in particular for neurons in the same brain region and during slow wave sleep.
Un problema important en la neurociència és determinar la connexió entre neurones utilitzant dades dels seus trens d’impulsos. Un mètode recent que afronta la detecció de connexions direccionals entre dinàmiques utilitzant processos puntuals és la mesura d’interdependència no lineal L. En aquesta tesi, utilitzem el model de Hindmarsh-Rose per testejar L en presència de soroll i per diferents règims dinàmics. Després comparem el desempenyorament de L en comparació al correlograma lineal i a dues mesures de trens d’impulsos. Finalment, apliquem totes aquestes mesures a dades d’impulsos de neurones obtingudes de senyals intracranials electroencefalogràfiques gravades durant una nit a un pacient amb epilèpsia. Quan utilitzem dades simulades, L demostra que és versàtil, robusta i més sensible que les mesures lineals. En canvi, utilitzant dades reals, les mesures lineals troben més connexions que L, especialment entre neurones en la mateixa àrea del cervell i durant la fase de son d’ones lentes.

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4

Guo, Bin. "A bio-inspired electronic nose micro-system based on integrated gas sensor array and log-spike processing /". View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20GUO.

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5

Chen, Hung Tat. "A portable electronic nose micro-system based on bio-inspired log-spike processing /". View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?ECED%202009%20CHEN.

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6

Armstrong, Brian Clement. "Processing techniques for improved radar detection in spiky clutter". Thesis, University College London (University of London), 1992. http://discovery.ucl.ac.uk/1317536/.

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The problem of improved radar detection of targets embedded in spiky clutter is addressed. Two main areas where improvements may be possible are investigated, namely improved clutter suppression by doppler filtering, and improved Constant False Alarm Rate (CFAR) processing. The clutter suppression performance of several doppler processors is quantified under a wide range of conditions. It is shown that in spatially homogeneous clutter ideal optimal (Hsiao) filters offer 2 to 3 dB higher improvement factor than conventional techniques. Adaptive Hsiao filters are evaluated under conditions of spatially heterogeneous clutter, and it is shown that practical losses due to filter adaptivity and spectral heterogeneity will outweigh the superior performance of ideal Hsiao filters in homogeneous clutter. It is concluded that improved doppler filtering offers little scope for improving detection performance in spiky clutter, and that more significant benefits are to be gained through improved CFAR processing. The performance of three current generation CFAR processors is evaluated in spatially uncorrelated K-distributed clutter to quantify detection losses. It is shown that losses of in excess of 10 dB can be expected in spiky clutter. Reducing the loss by exploitation of any spatial correlation of the underlying clutter power is investigated. To this end a mathematically rigorous model for spatially correlated K-distributed clutter is derived. An improved CFAR processor based on optimal weighting of reference cells is formulated and evaluated. It is shown that in highly correlated clutter CFAR loss can be reduced by 2 to 5 dB compared to Cell Averaging CFAR processors. An alternative "RDT-CFAR" processor is formulated to eliminate reliance on spatial correlation, and this is shown to reduce CFAR loss by more than 10 dB in spectrally homogeneous spiky clutter. However, an increase in false alarm rate in clutter without constant spectrum is demonstrated. The RDT-CFAR processor has been modified to eliminate dependence on surrounding range bins. The resulting "δ-CFAR" processor reduces CFAR loss by more than 10 dB in even moderately spiky clutter. It is also immune to extraneous targets and clutter edges, and its false alarm performance is insensitive to clutter spikiness.

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7

Shallwani, Aziz. "An adaptive playout algorithm with delay spike detection for real-time VoIP /". Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80143.

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As the Internet is a best-effort delivery network, audio packets may be delayed or lost en route to the receiver due to network congestion. To compensate for the variation in network delay, audio applications buffer received packets before playing them out. Basic algorithms adjust the packet playout time during periods of silence such that all packets within a talkspurt are equally delayed. Another approach is to scale individual voice packets using a dynamic time-scale modification technique based on the WSOLA algorithm.
In this work, an adaptive playout algorithm based on the normalized least mean square algorithm, is improved by introducing a spike-detection mode to rapidly adjust to delay spikes. Simulations on Internet traces show that the enhanced bi-modal playout algorithm improves performance by reducing both the average delay and the loss rate as compared to the original algorithm.

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8

Enatsu, Rei. "Usefulness of MEG magnetometer for spike detection in patients with mesial temporal epileptic focus". Kyoto University, 2008. http://hdl.handle.net/2433/124240.

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9

Elaraby, Nashwa. "ARCHITECTURE DESIGN FOR A NEURAL SPIKE-BASED DATA REDUCTION PLATFORM PROCESSING THOUSANDS OF RECORDING CHANNELS". Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/259825.

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Electrical Engineering
Ph.D.
Simultaneous recordings of single and multi-unit neural signals from multiple cortical areas in the brain are a vital tool for gaining more understanding of the operating mechanism of the brain as well as for developing Brain Machine Interfaces. Monitoring the activity levels of hundreds or even thousands of neurons can lead to reliable decoding of brain signals for controlling prosthesis of multiple degrees of freedom and different functionalities. With the advancement of high density microelectrode arrays, the craving of neuroscience research to record the activity of thousands of neurons is achievable. Recently CMOS-based Micro-electrode Arrays MEAs featuring high spatial and temporal resolution have been reported. The augmentation in the number of recording sites carries different challenges to the neural signal processing system. The primary challenge is the massive increase in the incoming data that needs to be transmitted and processed in real time. Data reduction based on the sparse nature of the neural signals with respect to time becomes essential. The dissertation presents the design of a neural spike-based data reduction platform that can handle a few thousands of channels on Field Programmable Gate Arrays (FPGAs), making use of their massive parallel processing capabilities and reconfigurability. For Standalone implementation the spike detector core uses Finite State Machines (FSMs) to control the interface with the data acquisition as well as sending the spike waveforms to a common output FIFO. The designed neural signal processing platform integrates the application of high-speed serial Multi-Gigabit transceivers on FPGAs to allow massive data transmission in real time. It also provides a design for autonomous threshold setting for each channel.
Temple University--Theses

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10

Scandellari, Carolina. "Algortimi di spike detection per applicazioni neuroprotesiche: sviluppo di modelli, implementazione e valutazione delle performance". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19904/.

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I disordini neurologici costituiscono il 6,3% delle cause di malattia in tutto il mondo, diventando una delle priorità della sanità globale. Per trattare questi disordini si utilizzano farmaci, ma alcuni pazienti possono risultarne resistenti. La Neuroingegneria propone soluzioni innovative per la cura e la riabilitazione di queste patologie, proponendo, tra le varie soluzioni, le neuroprotesi, capaci di sostituire un’area danneggiata del cervello o di ricollegare artificialmente due aree disconnesse bypassando la lesione che ha causato il danno. Tra questi, il dispositivo sviluppato presso la University of Kansas (KUMC) si è dimostrato essere efficace in esperimenti effettuati su topi con lesione focale in area motoria. Il funzionamento di questo dispositivo è basato sull’impianto di micro-elettrodi in due regioni cerebrali disconnesse a causa di una lesione. Questi creano un ponte in grado di ricollegare le due aree scollegate attraverso la registrazione di eventi (spike) in una delle due aree, e la seguente somministrazione di corrente nella seconda. In questo tipo di dispositivi, è importantissimo effettuare una identificazione corretta degli spikes. Il mio lavoro di tesi si inserisce nell’ambito della collaborazione tra il Rehab Technologies Lab (IIT, Genova), dove ho svolto il tirocinio, e la KUMC in relazione al progetto per lo sviluppo di neuroprotesi innovative per il recupero motorio a seguito di danni cerebrali. Nello specifico, il mio lavoro di Tesi si concentra sulla Spike Detection (SD), di cui uno dei problemi fondamentali è la mancanza di un ground truth, ovvero di una conoscenza a priori della localizzazione degli spikes nel tracciato. Nel contesto descritto sopra, si inseriscono gli obiettivi di questa Tesi: fornire un ground truth, studiare e adattare un set di algoritmi di SD già presenti in letteratura, modificare un algoritmo ad alte prestazioni sviluppato all’interno di IIT in passato e confrontare le prestazioni di tutti gli algoritmi di SD.

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11

Marcoux, Curtis. "Encoding of Sensory Signals Through Balanced Ionotropic Receptor Dynamics and Voltage Dependent Membrane Noise". Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34440.

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Encoding behaviorally relevant stimuli in a noisy background is critical for animals to survive in their natural environment. We identify core biophysical and synaptic mechanisms that permit the encoding of low frequency signals in pyramidal neurons of the weakly electric fish Apteronotus leptorhynchus, an animal that can accurately encode miniscule (0.1%) amplitude modulations of its self-generated electric field. We demonstrate that slow NMDA-R mediated EPSPs are able to summate over many interspike intervals of the primary electrosensory afferents (EAs), effectively eliminating the EA spike train serial correlations from the pyramidal cell input. This permits stimulus-evoked changes in EA spiking to be transmitted efficiently to downstream ELL pyramidal cells, where a dynamic balance of NMDA-R and GABA-A-R currents is critical for encoding low frequency signals. Interestingly, AMPA-R activity is depressed and plays a negligible role in the generation of action potentials; instead, cell intrinsic membrane noise implements voltage-dependent stochastic resonance to amplify weak sensory input and appears to drive a significant proportion of pyramidal cell spikes. Together, these mechanisms may be sufficient for the ELL to encode signals near the threshold of behavioral detection.

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12

September, Danwille Jacqwin Franco. "Detection and quantification of spice adulteration by near infrared hyperspectral imaging". Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6624.

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Thesis (MSc Food Sc)--University of Stellenbosch, 2011.
ENGLISH ABSTRACT: Near infrared hyperspectral imaging (NIR HSI) in conjunction with multivariate image analysis was evaluated for the detection of millet and buckwheat flour in ground black pepper. Additionally, midinfrared (MIR) spectroscopy was used for the quantification of millet and buckwheat flour in ground black pepper. These techniques were applied as they allow non-destructive, invasive and rapid analysis. Black pepper and adulterant (either millet or buckwheat flour) mixtures were made in 5% (w/w) increments spanning the range 0-100% (w/w). The mixtures were transferred to eppendorf tube holders and imaged with a sisuChema short wave infrared (SWIR) pushbroom imaging system across the spectral range of 1000–2498 nm. Principal component analysis (PCA) was applied to pseudo-absorbance images for the removal of unwanted data (e.g. background, shading effects and bad pixels). PCA was subsequently applied to the ‘cleaned’ data. An adulterant concentration related gradient was observed in principal component one (PC1) and a difference between black pepper adulterated with buckwheat and millet was noted in PC4. Four absorption peaks (1461, 2241, 2303 and 2347 nm) were identified in the loading line plot of PC1 that are associated with protein and oil. The loading line plot of PC4 revealed absorption peaks at 1955, 1999, 2136 and 2303 nm, that are related to protein and oil. Partial least squares discriminant analysis (PLS-DA) was applied to NIR HSI images for discrimination between black pepper adulterated with varying amounts of adulterant (millet or buckwheat). The model created with millet adulterated black pepper samples had a classification accuracy of 77%; a classification accuracy of 70% was obtained for the buckwheat adulterated black pepper samples. An average spectrum was calculated for each sample in the NIR HSI images and the resultant spectra were used for the quantification of adulterant (millet or buckwheat) in ground black pepper. All samples were also analysed using an attenuated total reflectance (ATR) Fourier transform (FT) – infrared (IR) instrument and MIR spectra were collected between 576 and 3999 cm-1. PLS regression was employed. NIR based predictions (r2 = 0.99, RMSEP = 3.02% (w/w), PLS factor = 4) were more accurate than MIR based predictions (r2 = 0.56, RMSEP = 19.94% (w/w), PLS factors = 7). Preprocessed NIR spectra revealed adulterant specific absorption bands (1743, 2112 and 2167 nm) whereas preprocessed MIR spectra revealed a buckwheat specific signal at 1574 cm-1. NIR HSI has great promise for both the qualitative and quantitative analysis of powdered food products. Our study signals the beginning of incorporating hyperspectral imaging in the analysis of powdered food substances and results can be improved with advances in instrumental development and better sample preparation.
AFRIKAANSE OPSOMMING: Die gebruik van naby infrarooi hiperspektrale beelding (NIR HB) tesame met veelvoudige beeldanalise is ondersoek vir die opsporing van stysel-verwante produkte (giers en bokwiet) in gemaalde swart pepper. Middel-infrarooi (MIR) spektroskopie is addisioneel gebruik vir die kwantifisering van hierdie stysel-verwante produkte in swart pepper. Albei hierdie tegnieke is toegepas aangesien dit deurdringend van aard is en dit bied nie-destruktiewe sowel as spoedige analise. Swart pepper en vervalsingsmiddel (giers of bokwiet) mengsels is uitgevoer in 5% (m/m) inkremente tussen 0 en 100% (m/m). Eppendorfbuishouers is met die mengsels gevul en hiperspektrale beelde is verkry deur die gebruik van ‘n sisuChema SWIR (kortgolf infrarooi) kamera met ‘n spektrale reikwydte van 1000–2498 nm. Hoofkomponent-analise (HK) is toegepas op pseudo-absorbansie beelde vir die verwydering van ongewenste data (bv. agtergrond, skadu en dooie piksels). Hoofkomponent-analise is vervolgens toegepas op die ‘skoon’ data. Hoofkomponent (HK) een (HK1) het die aanwesigheid van ‘n vervalsingsmiddel konsentrasie verwante gradient getoon terwyl HK4 ‘n verskil getoon het tussen swart pepper vervals met giers en bokwiet. Vier absorpsiepieke (1461, 2241, 2303 en 2347 nm) was geïdentifiseer binne die HK lading stip van HK1 wat met proteïen en olie geassosieer kon word. Die HK lading stip van HK4 het absorpsipieke by 1955, 1999, 2136 en 2303 nm aangedui wat verband hou met proteïen en olie. Parsiële kleinste waarde diskriminant-analise (PKW-DA) is toegepas op die hiperspektrale beelde vir die moontlike onderskeiding tussen swart pepper vervals met verskeie hoeveelhede vervalsingsmiddel (giers of bokwiet). ‘n Klassifikasie koers van 77% is verkry vir die model ontwikkel met giers vervalsde swart pepper terwyl die model ontwikkel met bokwiet vervalsde swarte pepper ‘n klassifikasie koers van 70% bereik het. ‘n Gemiddelde spektrum is bereken vir elke monster in die hiperspektrale beelde en die resulterende spektra is gebruik vir die kwantifisering van vervalsingsmiddels (giers of bokwiet) in gemaalde swart pepper. ‘n ATR FT-IR instrument met spektrale reikwydte van 576-3999 cm-1 is additioneel gebruik vir die analise van alle monsters. Parsiële kleinste waarde regressie is gebruik vir kwantifikasie doeleindes. NIR gebasseerde voorspellings (r2 = 0.99, RMSEP = 3.02% (m/m), PLS faktore = 4) was meer akkuraat as die MIR gebasseerde voorspellings (r2 = 0.56, RMSEP = 19.94% (m/m), PLS faktore = 7). Vooraf behandelde NIR spektra het vervalsingsmiddel verwante absorpsiepieke (1743, 2112 en 2167 nm) aangetoon terwyl vooraf behandelde MIR spektra ‘n bokwiet verwante absorpsiepiek by 1574 cm-1 aangedui het. NIR HB toon goeie potensiaal vir beide kwalitatiewe en kwantitatiewe analise van gepoeierde voedsel produkte. Ons studie kan gesien word as die begin van die inkorporasie van hiperspektrale beelding in die analise van gepoeierde voedsel material en verbeterde resulte kan verkry word deur die vordering in instrumentasie ontwikkeling en verbeterde monstervoorbereiding.

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13

Mielot, C. "Etude d'une cavité accélératrice supraconductrice de type spoke et de son coupleur de puissance associé". Phd thesis, Université Paris Sud - Paris XI, 2004. http://tel.archives-ouvertes.fr/in2p3-00023906.

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Cette thèse cofinancée par le CNRS et Framatome a porté sur l'étude d'une cavité supraconductrice de type spoke et de son coupleur de puissance associé. Les résultats ont été utilisés dans le cadre du 5ème PCRD et notamment pour l'accélérateur linéaire de protons de forte intensité(6mA) du projet de réacteur hybride XADS. La cavité (F=352MHz, BETA=0.35) a été testée à 4K et 2K. Ses performances à 4K donnent des marges confortables par rapport au cahier des charges XADS, ce qui est utile pour la fiabilité de l'installation. A 2K le champ accélérateur maximum est de 16MV/M ce qui est une référence mondiale.Le port de couplage de la cavité a été optimisé : sa position et son diamètre ont été modifiés pour diminuer les pertes HF sur l'antenne et limiter les risques de multipacting. Pour minimiser les pertes HF le mode de couplage avec la cavité choisie est électrique.Différents types de fenêtre céramique ont été étudiés afin de rendre fiable ce point critique du coupleur : disques coaxiaux avec ou sans chokes ou cylindrique creux coaxial . L'optimisation a porté sur la puissance réfléchie, les pertes dans la céramique, et le champ électrique de surface. Le disque muni de choke a également été modélisé et a fait l'objet d'une étude grâce à la théorie des lignes. L'ensemble des fenêtres a été étudié de manière systématique en fonction de différents paramètres géométriques. Le disque sans chokes semble un bon candidat pour notre application. La source de puissance sera un amplificateur état solide. Un coupleur entièrement coaxial est réalisable et sera fabriqué et testé prochainement.

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14

Fedele, Tommaso [Verfasser] y Benjamin [Akademischer Betreuer] Blankertz. "High-frequency electroencephalography (hf-EEG): Non-invasive detection of spike-related brain activity / Tommaso Fedele. Gutachter: Benjamin Blankertz". Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1066162689/34.

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15

Das, Mohammed. "Image analysis techniques for vertebra anomaly detection in X-ray images". Diss., Rolla, Mo. : University of Missouri--Rolla i.e. [Missouri University of Science and Technology], 2008. http://scholarsmine.mst.edu/thesis/MohammedDas_Thesis_09007dcc804c3cf6.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2008.
Degree granted by Missouri University of Science and Technology, formerly known as University of Missouri--Rolla. Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed March 24, 2008) Includes bibliographical references (p. 87-88).

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16

Quaglio, Pietro [Verfasser], Sonja Annemarie [Akademischer Betreuer] Grün y Björn Michael [Akademischer Betreuer] Kampa. "Detection and statistical evaluation of spike patterns in parallel electrophysiological recordings / Pietro Quaglio ; Sonja Annemarie Grün, Björn Michael Kampa". Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1218727659/34.

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17

Pumarica, Julio Cesar Saldaña. "Sistemas de detecção e classificação de impulsos elétricos de sinais neurais extracelulares". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3140/tde-19122016-133542/.

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O registro de sinais neurais através de matrizes de microeletrodos implantáveis no meio extracelular do córtex cerebral tem-se tornado um paradigma experimental para a neurociência. Por outro lado, a pesquisa recente sobre neuropróteses motoras tem mostrado que é possível decodificar comandos motores a partir dos sinais registrados no meio extracelular do córtex cerebral. Em ambos os contextos, neurociência experimental e desenvolvimento de neuropróteses motoras, um dos aspectos encontrados no estado da arte ´e a utilização de circuitos integrados (chips) implantados no cérebro. Nesses chips, os sinais neurais medidos com os microeletrodos são amplificados, filtrados, processados e transmitidos a um computador externo mediante fios que atravessam o crânio. Existe o interesse em desenvolver chips implantáveis que transmitam os sinais ao computador externo sem a necessidade de fios que atravessem o crânio. Na pesquisa do estado da arte tem-se encontrado a utilização de tais chips implantáveis sem fio em ratos e macacos, porém até a data da elaboração deste texto não foram encontrados relatos da aplicação em humanos. Um dos aspectos que deve se levar em consideração no desenvolvimento de interfaces neurais implantáveis sem fio é a largura de banda do canal de comunicação. Quanto maior a quantidade de dados a serem transmitidos, maior a largura de banda necessária e maior o aquecimento do chip devido à dissipação de potência. Esta tese aborda sistemas de processamento de sinais neurais extracelulares que tem como objetivo reduzir a quantidade de dados a serem transmitidos e assim viabilizar a transmissão sem fio. Para poder ser integrados dentro do chip implantável, esses sistemas de processamento devem estar otimizados em termos de área e consumo de potência. Dois processamentos encontrados na pesquisa de interfaces neurais implantáveis são a detecção de impulsos elétricos e a separação de impulsos elétricos (Spike Sorting). Nesta tese apresentam-se soluções para esses tipos de processamentos visando a implementação mediante tecnologia CMOS (Complementary Metal Oxide Semiconductor). Para o caso da detecção de impulsos elétricos (spikes), nesta tese apresenta-se uma alternativa de implementação em hardware de um operador matemático conhecido como operador não linear de energia (NEO do inglês Nonlinear Energy Operator) ou operador Teager. Através da aplicação desse operador a um sinal neural evidencia-se a presença de spikes e atenua-se o ruído. Uma das características inovadoras da implementação apresentada nesta tese é a utilização de um circuito elevador ao quadrado que consiste de apenas três transistores, como bloco funcional básico para a realização da operação NEO. O circuito NEO desenvolvido consome 300 pJ no processamento de um spike e foi caracterizado por simulação até em 30 kHz, frequência que é compatível com as taxas de amostragem encontradas na literatura. O outro processamento abordado nesta tese, conhecido como separação de impulsos elétricos ou Spike Sorting, consiste no agrupamento dos impulsos elétricos registrados por um eletrodo em categorias, de maneira que em uma categoria estejam os impulsos gerados por um mesmo neurônio. Em outras palavras, o objetivo é reconhecer quais dos impulsos elétricos medidos pelo eletrodo pertencem a um mesmo neurônio, sendo possível que vários neurônios influenciem na medida realizada por um único eletrodo. Uma solução para a separação de impulsos, apropriada no contexto de sistemas implantáveis, é o template matching. Essa técnica baseia-se na geração de modelos (templates) durante uma fase inicial ao final da qual o número de templates gerados corresponde ao número de neurônios identificados pelo eletrodo. Numa fase seguinte, o sistema associa cada impulso elétrico detectado a um dos modelos inicialmente gerados. Nesta tese propõe-se um sistema de classificação que executa essa segunda fase do processo de spike sorting. Nesta tese apresenta-se o projeto de um sistema de classificação de spikes baseado na t écnica template matching, implementado com tecnologia CMOS. A implementação proposta nesta tese baseia-se na representação de amostras analógicas mediante o tempo. Esse tipo de representação de sinais analógicos mediante atrasos de pulsos digitais está sendo muito utilizado como alternativa à representação no domínio da tensão, da corrente ou da carga elétrica. A vantagem desse tipo de representação é que não se vê severamente afetada pela redução da tensão de alimentação dos circuitos integrados fabricados em tecnologias submicrométricas. A taxa de acerto na classificação do sistema desenvolvido é maior que 99% inclusive considerando um offset de até 20mV no comparador de saída. Os circuitos apresentados neste trabalho foram projetados considerando dispositivos da tecnologia TSMC de 90nm.
Neural signals recording through implantable microelectrode arrays in cortex extracellular medium has become an experimental paradigm for neuroscience. Moreover, recent research about motor neuroprostheses has shown that it is possible to decode motor commands from the signals recorded in the cerebral cortex extracellular medium. In both situations, experimental neuroscience and motor neuroprostheses development, one of the issues encountered in the state-of-the-art is the use of integrated circuits (chips) implanted in the brain. In these chips, neural signals measured with microelectrodes are amplified, filtered, processed, and transmitted to an external computer through wires that run through the skull. There is interest in developing implantable chips that transmit signals to the external computer without the need for wires passing through the skull. In the survey of the state-of-the-art it has found the use of such implantable wireless chips in rats and monkeys, but until the date of this writing we have not found reports of application in humans. One of the aspects that must be taken into account in the development of wireless implantable neural interfaces is the bandwidth of the communication channel. The greater the amount of data to be transmitted, the greater the bandwidth required and higher chip heating due to power dissipation. This thesis deals with extracellular neural signals processing systems that aim to reduce the amount of data to be transmitted and in this way to enable wireless transmission. In order to integrate them into an implantable chip, those processing systems must be optimized in terms of area and power consumption. Two processes found in the research of implantable neural interfaces are spike detection and spike sorting. In this thesis solutions for these types of processing are presented considering their implementation by CMOS (Complementary Metal Oxide Semiconductor). For the case of spike detection in this thesis it is presented an alternative for the hardware implementation of a mathematical operator known as NEO (Nonlinear Energy Operator). Through the application of this operator to a neural signal the presence of spikes becomes evident and the noise is attenuated. One of the innovative characteristics of the implementation presented in this thesis is the use of a squarer circuit which consists of only three transistors, as a basic function block for performing operation of NEO. NEO circuit consumes 300 pJ in processing a spike, and was characterized by simulation up to 30 kHz, frequency which is compatible with sampling rates found in the literature. The other processing discussed in this thesis, known as Spike Sorting, is the grouping of electrical impulses recorded by an electrode into categories so that the spikes belonging to the same category were generated by a single neuron. In other words, the goal is to recognize which of the spikes measured by the electrode belong to the same neuron, given that it is possible that several neurons influence the measure performed by a single electrode. A solution for the Spike Sorting suitable in the context of implantable systems, is the template matching. This technique is based on generating templates during an initial phase at the end of which the number of generated templates corresponds to the number of neurons identified by the electrode. In the next phase, the system associates each detected spike to one of the templates generated initially. In this thesis it is proposed a classification systems which performs that second phase of the spike sorting process. This thesis presents the design of a spike classification system based on template matching technique, implemented in CMOS technology. The processing proposed in this work is based on the time-based representation of the analog samples. This kind of representation of analog signals by delays of digital pulses is being widely used as an alternative to the classical representation of samples by voltage, current or electric charge. The advantage of this time-mode representation is that it is not severely affected by reduced supply voltage of integrated circuits manufactured in sub-micrometer technologies. The classification hit rate of the developed system is greater than 99% even when an offset of 20 mV is assumed for the output comparator. All the circuits presented in this work were designed using devices from TSMC 90nm technology.

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18

Hsu, Ming-Hsuan. "MICROPROCESSOR-COMPATIBLE NEURAL SIGNAL PROCESSING FOR AN IMPLANTABLE NEURODYNAMIC SENSOR". Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1244237706.

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19

Melano, Timothy. "Insect-Machine Interfacing". Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145388.

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A terrestrial robotic electrophysiology platform has been developed that can hold a moth (Manduca sexta), record signals from its brain or muscles, and use these signals to control the rotation of the robot. All signal processing (electrophysiology, spike detection, and robotic control) was performed onboard the robot with custom designed electronic circuits. Wireless telemetry allowed remote communication with the robot. In this study, we interfaced directionally-sensitive visual neurons and pleurodorsal steering muscles of the mesothorax with the robot and used the spike rate of these signals to control its rotation, thereby emulating the classical optomotor response known from studies of the fly visual system. The interfacing of insect and machine can contribute to our understanding of the neurobiological processes underlying behavior and also suggest promising advancements in biosensors and human brain-machine interfaces.

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20

Hassanpour, Hamid. "Time-frequency based detection of newborn EEG seizure". Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15853/1/Hamid_Hassanpour_Thesis.pdf.

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Neurological diseases in newborns are usually first revealed by seizures, which are characterised by a synchronous discharge of a large number of neurons. Failure to control seizures may lead to brain damage or even death. The importance of this problem prompted many researchers to look for accurate automatic methods for seizure detection. Nonstationarity and multicomponent behaviour of newborn EEG signals made this task very challenging. The significant overlap in the characteristic of background and seizure activities in newborn EEG signals added to the difficulty of seizure detection. This research uses time-frequency based methods for automatic seizure detection. Since time-frequency signal analysis methods use joint representation in both time and frequency domains, they proved to be very suitable for analysis and processing of nonstationary and multicomponent signals such as newborn EEG. Before using any seizure detector, the EEG data is pre-processed in order to reduce the noise effects using a time-frequency based technique. The proposed method is based on the singular value decomposition (SVD) technique applied to the matrix representing the time-frequency distribution (TFD) of the EEG signal. It has been shown that by appropriately filtering the singular vectors associated with the TFD, one can effectively enhance the desired information embedded in the signal. Neonatal EEG seizures can have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. The seizure detection techniques proposed in the literature concentrated on using either low frequency or high frequency signatures but not both simultaneously. These methods tend to miss the seizures that reveal themselves only in one of the two frequency areas. In this research, we propose a detection method that uses seizure features in both low and high frequency areas. To detect EEG seizures using the low frequency signatures, an SVD-based technique is employed. The technique uses the estimated distribution function of the singular vectors associated with the time-frequency distribution of EEG epochs to discriminate between seizure and nonseizure patterns. The high frequency signatures of seizures are mostly the result of spike events in the EEG signals. To detect these spike events, the signal is mapped into the TF domain. The high instantaneous energy of spikes is reflected as a localised energy in the high frequency area of the TF domain. Consequently, a spike can be seen as a ridge in this area of the TF domain. It has been shown that during seizure activity there is regularity in the distribution of the interspike intervals. This feature has been used as the basis for discriminating between seizure and nonseizure patterns. The performance results obtained by applying the proposed methods on EEG signals extracted from a number of newborns show the superiority of these methods over the existing ones.

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21

Hassanpour, Hamid. "Time-Frequency Based Detection of Newborn EEG Seizure". Queensland University of Technology, 2004. http://eprints.qut.edu.au/15853/.

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Neurological diseases in newborns are usually first revealed by seizures, which are characterised by a synchronous discharge of a large number of neurons. Failure to control seizures may lead to brain damage or even death. The importance of this problem prompted many researchers to look for accurate automatic methods for seizure detection. Nonstationarity and multicomponent behaviour of newborn EEG signals made this task very challenging. The significant overlap in the characteristic of background and seizure activities in newborn EEG signals added to the difficulty of seizure detection. This research uses time-frequency based methods for automatic seizure detection. Since time-frequency signal analysis methods use joint representation in both time and frequency domains, they proved to be very suitable for analysis and processing of nonstationary and multicomponent signals such as newborn EEG. Before using any seizure detector, the EEG data is pre-processed in order to reduce the noise effects using a time-frequency based technique. The proposed method is based on the singular value decomposition (SVD) technique applied to the matrix representing the time-frequency distribution (TFD) of the EEG signal. It has been shown that by appropriately filtering the singular vectors associated with the TFD, one can effectively enhance the desired information embedded in the signal. Neonatal EEG seizures can have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. The seizure detection techniques proposed in the literature concentrated on using either low frequency or high frequency signatures but not both simultaneously. These methods tend to miss the seizures that reveal themselves only in one of the two frequency areas. In this research, we propose a detection method that uses seizure features in both low and high frequency areas. To detect EEG seizures using the low frequency signatures, an SVD-based technique is employed. The technique uses the estimated distribution function of the singular vectors associated with the time-frequency distribution of EEG epochs to discriminate between seizure and nonseizure patterns. The high frequency signatures of seizures are mostly the result of spike events in the EEG signals. To detect these spike events, the signal is mapped into the TF domain. The high instantaneous energy of spikes is reflected as a localised energy in the high frequency area of the TF domain. Consequently, a spike can be seen as a ridge in this area of the TF domain. It has been shown that during seizure activity there is regularity in the distribution of the interspike intervals. This feature has been used as the basis for discriminating between seizure and nonseizure patterns. The performance results obtained by applying the proposed methods on EEG signals extracted from a number of newborns show the superiority of these methods over the existing ones.

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22

Guo, Lilin. "A Biologically Plausible Supervised Learning Method for Spiking Neurons with Real-world Applications". FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2982.

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Learning is central to infusing intelligence to any biologically inspired system. This study introduces a novel Cross-Correlated Delay Shift (CCDS) learning method for spiking neurons with the ability to learn and reproduce arbitrary spike patterns in a supervised fashion with applicability tospatiotemporalinformation encoded at the precise timing of spikes. By integrating the cross-correlated term,axonaland synapse delays, the CCDS rule is proven to be both biologically plausible and computationally efficient. The proposed learning algorithm is evaluated in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. The results indicate that the proposed CCDS learning rule greatly improves classification accuracy when compared to the standards reached with the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. Network structureis the crucial partforany application domain of Artificial Spiking Neural Network (ASNN). Thus, temporal learning rules in multilayer spiking neural networks are investigated. As extensions of single-layer learning rules, the multilayer CCDS (MutCCDS) is also developed. Correlated neurons are connected through fine-tuned weights and delays. In contrast to the multilayer Remote Supervised Method (MutReSuMe) and multilayertempotronrule (MutTmptr), the newly developed MutCCDS shows better generalization ability and faster convergence. The proposed multilayer rules provide an efficient and biologically plausible mechanism, describing how delays and synapses in the multilayer networks are adjusted to facilitate learning. Interictalspikes (IS) aremorphologicallydefined brief events observed in electroencephalography (EEG) records from patients with epilepsy. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for seizure prediction and guided therapy. In this work, we present a new IS detection method using the Wavelet Encoding Device (WED) method together with CCDS learning rule and a specially designed Spiking Neural Network (SNN) structure. The results confirm the ability of such SNN to achieve good performance for automatically detecting such events from multichannel EEG records.

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23

Balasubramanian, Karthikeyan. "Reconfigurable System-on-Chip Architecture for Neural Signal Processing". Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/144255.

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Electrical Engineering
Ph.D.
Analyzing the brain's behavior in terms of its neuronal activity is the fundamental purpose of Brain-Machine Interfaces (BMIs). Neuronal activity is often assumed to be encoded in the rate of neuronal action potential spikes. Successful performance of a BMI system is tied to the efficiency of its individual processing elements such as spike detection, sorting and decoding. To achieve reliable operation, BMIs are equipped with hundreds of electrodes at the neural interface. While a single electrode/tetrode communicates with up to four neurons at a given instant of time, a typical interface communicates with an ensemble of hundreds or even thousands of neurons. However, translation of these signals (data) into usable information for real-time BMIs is bottlenecked due to the lack of efficient real-time algorithms and real-time hardware that can handle massively parallel channels of neural data. The research presented here addresses this issue by developing real-time neural processing algorithms that can be implemented in reconfigurable hardware and thus, can be scaled to handle thousands of channels in parallel. The developed reconfigurable system serves as an evaluation platform for investigating the fundamental design tradeoffs in allocating finite hardware resources for a reliable BMI. In this work, the generic architectural layout needed to process neural signals in a massive scale is discussed. A System-on-Chip design with embedded system architecture is presented for FPGA hardware realization that features (a) scalability (b) reconfigurability, and (c) real-time operability. A prototype design incorporating a dual processor system and essential neural signal processing routines such as real-time spike detection and sorting is presented. Two kinds of spike detectors, a simple threshold-based and non-linear energy operator-based, were implemented. To achieve real-time spike sorting, a fuzzy logic-based spike sorter was developed and synthesized in the hardware. Furthermore, a real-time kernel to monitor the high-level interactions of the system was implemented. The entire system was realized in a platform FPGA (Xilinx Virtex-5 LX110T). The system was tested using extracellular neural recordings from three different animals, a owl monkey, a macaque and a rat. Operational performance of the system is demonstrated for a 300 channel neural interface. Scaling the system to 900 channels is trivial.
Temple University--Theses

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24

Nguyen, Van Dong. "Speciation analysis of butyl- and phenyltin compounds in environmental samples by GC separation and atomic spectrometric detection". Doctoral thesis, Umeå : Department of Chemistry, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-892.

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25

Chamorro, Claudia Carranza. "Genetic diversity of avian coronavirus infectious bronchitis detected from commercial poultry in Brazil". Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/10/10134/tde-04032016-154921/.

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Infectious bronchitis virus (IBV) is the causative agent of an economically important disease of poultry. In Brazil this disease causes respiratory, renal and reproductive problems in birds of all ages, despite constant vaccination with the Massachusetts strain H120. This lack of immunological protection is known to be due the genetic variation in the spike glycoprotein of IBV, which is involved in host cell attachment, neutralization and the induction of protective immunity. Brazilian IBV variants resulting of this genetic variation are present since the 80s and this study aimed to epidemiologicaly analyze and molecularly characterize the existing variants during 2010-2015 and perform a bioinformatics analysis of the available sequences of IBV variants in a 40 year period. Of the 453 samples tested, 61.4% were positive for IBV and 75.9% of them were considered variants and were detected in birds of all ages, distributed in all five Brazilian regions. A fragment of 559-566 bp was obtained from 12 isolates, where BR-I was the predominant variant while only one isolate belonged to the BR-II genotype. Bioinformatics analysis of the sequences of 40 years of Brazilian IBV variants was performed and the ratio of non-synonymous substitutions per non-synonymous site (dn) to synonymous substitutions per synonymous site (ds) dN/dS was calculated. It revealed a predominance of codons with non-synonymous substitutions in the first third of the S1 gene and a dN/dS ratio of 0.6757, indicating that this portion of the gene was under negative selection. Additionally prediction of N-glycosilation sites showed that most of the BR-I variants (from 2003 to early 2014) present an extra site at animoacid position 20, while the newest ones lack this feature.Together these results suggest that IBV Brazilian variants had probably suffered drastic mutations in some points between the years 1983 to 2003 and after achieving an antigenic structure effective enough for invasion and replication in their hosts, the selection processes became silent.
O vírus da bronquite infecciosa das galinhas (IBV) é o agente causador de uma doença aviária economicamente importante. No Brasil, esta doença ocasiona problemas respiratórios, renais e reprodutivos em aves de todas as idades, apesar da vacinação constante com a cepa Massachusetts H120. Esta falha na proteção conferida pela vacina é ocasionada por mutações nos nucleotídeos do gene da glicoproteína da espícula, a qual está envolvida no processo de interação comas células do hospedeiro, a neutralização e a indução de imunidade protetora. As variantes brasileiras resultantes dessa mutação genética estão presentes desde os anos 80 e este estudo teve como objetivo analisar epidemiologicamente e caracterizar molecularmente os vírus variantes existentes durante 2010-2015 e realizar uma análise bioinformática das sequências disponíveis no GenBank em um período de 40 anos. Das 453 amostras analisadas, 61,4% foram positivas para IBV e 75,9% delas foram consideradas variantes e foram detectados em aves de todas as idades, distribuídos em todas as 5 regiões do Brasil. Um fragmento de 559-566 pb foi obtido a partir de 12 isolados, onde BR-I foi a variante predominante ao contrario que apenas um isolado pertencia ao genótipo BR-II. Análise bioinformática de 40 anos de variantes do IBV brasileiros revelou uma predominância de codões com as substituições não sinónimos no primeiro terço do gene S1 e uma relação dN / dS de 0,6757, indicando que esta porção do gene estava sob selecção negativa. Além disso a previsão de pontos de de N-glicosilação mostrou que a maioria das amostras variantes BR-I (entre o 2003 e início de 2014) apresentam um ponto adicional na posição 20, enquanto as variantes mais novas não apresentam esse ponto de nglicosilação. Estes resultados sugerem que as variantes brasileiras teriam sofrido mutações provavelmente drásticas em alguns pontos do genoma, entre os anos de 1983 a 2003 e depois de atingir uma estrutura antigênica eficaz o suficiente para a invasão e replicação em seus hospedeiros, o processo de seleção mudou para seleção negativa.

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26

Nonclercq, Antoine. "New strategies of acquisition and processing of encephalographic biopotentials". Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210711.

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Electroencephalography is a medical diagnosis technique. It consists in measuring the biopotentials produced by the upper layers of the brain at various standardized places on the skull.

Since the biopotentials produced by the upper parts of the brain have an amplitude of about one microvolt, the measurements performed by an EEG are exposed to many risks.

Moreover, since the present tendency is measure those signals over periods of several hours, or even several days, human analysis of the recording becomes extremely long and difficult. The use of signal analysis techniques for the help of paroxysm detection with clinical interest within the electroencephalogram becomes therefore almost essential. However the performance of many automatic detection algorithms becomes significantly degraded by the presence of interference: the quality of the recordings is therefore fundamental.

This thesis explores the benefits that electronics and signal processing could bring to electroencephalography, aiming at improving the signal quality and semi-automating the data processing.

These two aspects are interdependent because the performance of any semi-automation of the data processing depends on the quality of the acquired signal. Special attention is focused on the interaction between these two goals and attaining the optimal hardware/software pair.

This thesis offers an overview of the medical electroencephalographic acquisition chain and also of its possible improvements.

The conclusions of this work may be extended to some other cases of biological signal amplification such as the electrocardiogram (ECG) and the electromyogram (EMG). Moreover, such a generalization would be easier, because their signals have a wider amplitude and are therefore more resistant toward interference.

Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

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27

Quotb, Adam. "Méthodes et systèmes pour la détection adaptative et temps réel d’activité dans les signaux biologiques". Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14595/document.

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L’intéraction entre la biologie et l’électronique est une discpline en pleine essort. De nom-breux systèmes électroniques tentent de s’interconnecter avec des tissus ou des cellules vivantesaﬁn de décoder l’information biologique. Le Potentiel d’action (PA) est au coeur de codagebiologique et par conséquent il est nécéssaire de pouvoir les repérer sur tout type de signal bio-logique. Par conséquent, nous étudions dans ce manuscrit la possibilité de concevoir un circuitélectronique couplé à un système de microélectrodes capable d’eﬀectuer une acquisition, unedétection des PAs et un enregistrement des signaux biologiques. Que ce soit en milieu bruitéou non, nous considérons le taux de détection de PA et la contrainte de temps réel commedes notions primordiales et la consommation en silicium comme un prix à payer. Initialementdéveloppés pour l’étude de signaux neuronaux et pancréatiques, ces systèmes conviennent par-faitement pour d’autres type de cellules
Interaction between biology and electronic is in expansion. Many electronic systems aretrying to interconnect with tissues or living cells to decode biological information. The ActionPotential (AP) is the heart of biological coding and therefore it is necessary to be able to locateit from any type of biological signal. Therefore, we study in this manuscript the possibility ofdesigning an electronic circuit coupled to microelectrodes capable of acquisition, detection ofPAs and recording of biological signals. Whether or not in a noisy environment, we consider thedetection rate of PA and the real time-computing constraint as an hard speciﬁcationand andsilicon area as a price to pay. Initially developed for the study of neural signals and pancreatic,these systems are ideal for other types of cells

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28

Prado, Saldaña Víctor Zacarías. "Análisis y comprobación del comportamiento de los transistores de efecto de campo sensibles a iones respecto a los MOSFETS". Bachelor's thesis, Pontificia Universidad Católica del Perú, 2008. http://tesis.pucp.edu.pe/repositorio/handle/123456789/225.

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El objetivo del presente trabajo es analizar y comprobar el comportamiento de los ISFETs respecto a los MOSFETs. Para lograr lo anterior se utilizarán técnicas modernas de simulación y ensayos en laboratorio; de esta forma se podrá observar las similitudes y diferencias de comportamiento, y dependencias entre las variaciones tales como corriente de drenador respecto a variaciones del voltaje umbral. Además, esta comprobación se realizará considerando los valores de los parámetros de fabricación que ha generado el fabricante de los ISFETs y asumiendo valores intrínsecos de este dispositivo y siempre observando lo que sucede con los MOSFETs en situaciones similares.
Tesis

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29

Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing". Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16200/1/Luke_Rankine_Thesis.pdf.

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Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.

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30

Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing". Queensland University of Technology, 2006. http://eprints.qut.edu.au/16200/.

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Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.

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31

Jakubíček, Roman. "Metody segmentace a identifikace deformovaných obratlů ve 3D CT datech onkologických pacientů". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433053.

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In this doctoral thesis, the design of algorithms enabling the implementation of a fully automatic system for vertebrae segmentation in 3D computed tomography (CT) image data of possibly incomplete spines, in patients with bone metastases and vertebral compressions is presented. The proposed algorithm consists of several fundamental problems: spine detection and its axis determination, individual vertebra localization and identification (labeling), and finally, precise segmentation of vertebrae. The detection of the spine, specifically identifying its ends, and determining the course of the spinal canal, combines several advanced methods, including deep learning-based approaches. A novel growing circle method has been designed for tracing the spinal cord canal. Further, the innovative spatially variant filtering of brightness profiles along the spine axis leading to intervertebral disc localization has been proposed and implemented. The discs thus obtained are subsequently identified via comparing the tested vertebrae and model of vertebrae provided by a machine-learning process and optimized by dynamic programming. The final vertebrae segmentation is provided by the deformation of the complete-spine intensity model, utilizing a proposed multilevel registration technique. The complete proposed algorithm has been validated on testing databases, including also publicly available datasets. This way, it has been proven that the newly proposed algorithms provide results at least comparable to other author’s algorithms, and in some cases, even better. The main strengths of the algorithms lie in high reliability of the results and in the robustness to even strongly distorted vertebrae of oncological patients and to the occurrence of artifacts in data; moreover, they are capable of identifying the vertebra labels even in incomplete spinal CT scans. The strength is also in the complete automation of the processing and in its relatively low computational complexity enabling implementation on standard PC hardware. The system for fully automatic localization and labeling of distorted vertebrae in possibly incomplete spinal CT data is presented in this doctoral thesis. The design of algorithms enabling the implementation utilizes several novel approaches, which were presented at international conferences and published in the journal Jakubicek et al. (2020). Based on the results of the experimental validation, the proposed algorithms seem to be routinely usable and capable of providing fully acceptable input data (identified and precisely segmented vertebrae) as needed in the subsequent automatic spine bone lesion analysis.

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32

Montanari, Giovanni. "Deep Transfer Learning for Automated Detection of Spinal Lesions from CT scans". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In this thesis we implement an automated Computer-Aided Detection (CADe) system for spine lesions using Computed Tomographies (CTs) and Convolutional Neural Networks (CNNs). To this end, we conceptualize an algorithmic approach for the whole process of extraction and processing of the vertebrae from CT scans, which also manages the detection step for the whole vertebral body. For training and testing purposes, we generated a dataset composed of several CTs in collaboration with the Rizzoli Orthopaedic Insitute of Bologna, Italy. The vertebrae, either healthy or containing lesions (e.g. metastases, primary tumors, lytic and sclerotic lesions) were extracted from CT scans with a toolbox developed ad hoc to automatize the process. The resulting dataset is composed of slices from the previously extracted volumes containing the vertebrae. Slices were processed with contrast enhancement and data augmentation techniques, and subsequently used to train the Neural Network. For the purpose of detection, we perform an in-depth comparative study by implementing 4 pre-trained networks and exploiting Transfer Learning techniques. To prove the great advantages of Transfer Learning, we show how the pre-trained networks outperform a network trained from scratch, reaching 95.97% accuracy and F1 score of 94.22%. Finally, we equip the CADe system with an intuitive Graphical User Interface (GUI) to allow physicians to use the automated detection software as a support tool for diagnoses on new patients.

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33

Zhang, Han. "Design of a high gain filter system for Marker Locator". Thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25021.

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This paper introduces a high-gain, low-noise band-pass filter system for detection/amplification of small signals. In addition, related theory and methodology are described for a specific design implementation. Simulation and experimental results are presented and discussed. The purpose of the implemented design was to construct a band-pass filter system with 102 dB gain and with an output noise level of less than 0.8V. The design of the high-gain band-pass filter system was achieved mainly with the help of Filter Pro, LTSpice IV, and Multisim 12. The thesis provides important support for the project Marker Locator and constitutes a valuable reference for future active filter system design and small signal detection/amplification.
Marker Locator

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34

Chmelík, Jiří. "Metody detekce, segmentace a klasifikace obtížně definovatelných kostních nádorových lézí ve 3D CT datech". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433066.

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The aim of this work was the development of algorithms for detection segmentation and classification of difficult to define bone metastatic cancerous lesions from spinal CT image data. For this purpose, the patient database was created and annotated by medical experts. Successively, three methods were proposed and developed; the first of them is based on the reworking and combination of methods developed during the preceding project phase, the second method is a fast variant based on the fuzzy k-means cluster analysis, the third method uses modern machine learning algorithms, specifically deep learning of convolutional neural networks. Further, an approach that elaborates the results by a subsequent random forest based meta-analysis of detected lesion candidates was proposed. The achieved results were objectively evaluated and compared with results achieved by algorithms published by other authors. The evaluation was done by two objective methodologies, technical voxel-based and clinical object-based ones. The achieved results were subsequently evaluated and discussed.

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35

Hammarberg, Björn. "A Signal Processing Approach to Practical Neurophysiology : A Search for Improved Methods in Clinical Routine and Research". Doctoral thesis, Uppsala University, Signals and Systems Group, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-1957.

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Signal processing within the neurophysiological field is challenging and requires short processing time and reliable results. In this thesis, three main problems are considered.

First, a modified line source model for simulation of muscle action potentials (APs) is presented. It is formulated in continuous-time as a convolution of a muscle-fiber dependent transmembrane current and an electrode dependent weighting (impedance) function. In the discretization of the model, the Nyquist criterion is addressed. By applying anti-aliasing filtering, it is possible to decrease the discretization frequency while retaining the accuracy. Finite length muscle fibers are incorporated in the model through a simple transformation of the weighting function. The presented model is suitable for modeling large motor units.

Second, the possibility of discerning the individual AP components of the concentric needle electromyogram (EMG) is explored. Simulated motor unit APs (MUAPs) are prefiltered using Wiener filtering. The mean fiber concentration (MFC) and jitter are estimated from the prefiltered MUAPs. The results indicate that the assessment of the MFC may well benefit from the presented approach and that the jitter may be estimated from the concentric needle EMG with an accuracy comparable with traditional single fiber EMG.

Third, automatic, rather than manual, detection and discrimination of recorded C-fiber APs is addressed. The algorithm, detects the Aps reliably using a matched filter. Then, the detected APs are discriminated using multiple hypothesis tracking combined with Kalman filtering which identifies the APs originating from the same C-fiber. To improve the performance, an amplitude estimate is incorporated into the tracking algorithm. Several years of use show that the performance of the algorithm is excellent with minimal need for audit.

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36

Věžníková, Romana. "Detekce a identifikace typu obratle v CT datech onkologických pacientů". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316851.

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Automated spine or vertebra detection and segmentation from CT images is a difficult task for several reasons. One of the reasons is unclear vertebra boundaries and indistinct boundaries between vertebra. Next reason is artifacts in images and high degree of anatomical complexity. This paper describes the design and implementation of vertebra detection and classification in CT images of cancer patients, which adds to the complexity because some of vertebrae are deformed. For the vertebra segmentation, the Otsu’s method is used. Vertebra detection is based on search of borders between individual vertebra in sagittal planes. Decision trees or the generalized Hough transform is applied for the identification whereas the vertebra searching is based on similarity between each vertebra model shape and planes of CT scans.

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37

Huneau, Clément. "Détection et modélisation biomathématique d'évènements transitoires dans les signaux EEG intracérébraux : application au suivi de l'épileptogenèse dans un modèle murin". Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00869599.

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Les épilepsies acquises se déclarent après un processus graduel appelé épileptogenèse. Bien que cliniquement silencieux, ce processus implique des modifications fonctionnelles observables notamment par électroencéphalographie. Cette thèse vise i) à identifier des marqueurs électrophysiologiques apparaissant au cours de l'épileptogenèse, et ii) à comprendre les modifications physiopathologiques sous-jacentes responsables de ces marqueurs et de leur évolution temporelle. Dans un premier temps, nous avons, dans un modèle d'épilepsie partielle chez la souris, monitoré des signaux électrophysiologiques intracérébraux pendant la mise en place de la maladie. Nous avons observé dans ces signaux expérimentaux, l'émergence d'événements transitoires pathologiques appelés pointes épileptiques. Nous avons développé des méthodes de traitement du signal pour détecter et caractériser automatiquement ces événements. Ainsi, nous avons pu mettre en évidence certains changements dans la forme des pointes épileptiques au cours de l'épileptogenèse ; en particulier l'apparition et l'augmentation d'une onde qui suit la pointe épileptique. Une hypothèse défendue dans ces travaux est que ces changements morphologiques peuvent constituer des marqueurs de l'épileptogenèse dans ce modèle animal. Dans un second temps, afin d'interpréter ces modifications électrophysiologiques en termes de processus neurophysiologiques sous-jacents, nous avons implémenté un modèle biomathématique, physiologiquement argumenté, capable de simuler des pointes épileptiques. Formellement, ce modèle est un système dynamique non linéaire qui reproduit les interactions synaptiques (excitatrices et inhibitrices) dans une population de neurones. Une analyse de sensibilité de ce modèle a permis de mettre en évidence le rôle critique de certains paramètres de connectivité dans la morphologie des pointes. Nos résultats montrent en effet, qu'une diminution de l'inhibition GABAergique entraîne un accroissement de l'onde dans les pointes épileptiques. À partir du modèle théorique, nous avons pu ainsi émettre des hypothèses sur les modifications opérant au cours du processus d'épileptogenèse. Ces hypothèses ont pu être en partie vérifiées expérimentalement en bloquant artificiellement l'inhibition GABAergique, dans le modèle in vivo chez la souris, et dans un modèle in vitro chez le rat. En conclusion, ce travail de thèse fournit, dans un modèle animal, un biomarqueur électrophysiologique de l'épileptogenèse et tente d'expliquer, grâce à une modélisation biomathématique, les processus neurophysiologiques sous-jacents qu'il reflète.

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38

SPROULE, MICHAEL. "Spike train propagation in the axon of a visual interneuron, the descending contralateral movement detector of Locusta migratoria". Thesis, 2011. http://hdl.handle.net/1974/6830.

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Neurons perform complex computations, communications and precise transmissions of information in the form of action potentials (APs). The high level of heterogeneity and complexity at all levels of organization within a neuron and the functional requirement of highly permeable cell membranes leave neurons exposed to damage when energy levels are insufficient for the active maintenance of ionic gradients. When energy is limiting the ionic gradient across a neuron’s cell membrane risks being dissipated which can have dire consequences. Other researchers have advocated “generalized channel arrest” and/or “spike arrest” as a means of reducing the neuronal permeability allowing neurons to adjust the demands placed on their electrogenic pumps to lower levels of energy supply. I investigated the consequences of hypoxia on the propagation of a train of APs down the length of a fast conducting axon capable of transmitting APs at very high frequencies. Under normoxic conditions I found that APs show conduction velocities and instantaneous frequencies nearly double that of neurons experiencing energy limiting hypoxic conditions. I show that hypoxia affects AP conduction differently for different lengths of axon and for APs of different instantaneous frequencies. Action potentials of high instantaneous frequency in branching lengths of axon within ganglia were delayed more significantly than those in non-branching lengths contained within the connective and fail preferentially in branching axon. I found that octopamine attenuates the effects of hypoxia on AP propagation for the branching length of axon but has no effect on the non-branching length of axon. Additionally, for energetically stable cells, application of the anti-diabetic medication metformin or the hyperpolarization-activated cyclic nucleotide-gated (HCN) channel blocker ZD7288 resulted in a reduced performance similar to that seen in neurons experiencing energetic stress. Furthermore both metformin and ZD7288 affect the shape of individual APs within an AP train as well as the original temporal sequence of the AP train, which encodes behaviourally relevant information. I propose that the reduced performance observed in an energetically compromised cell represents an adaptive mechanism employed by neurons in order to maintain the integrity of their highly heterogeneous and complex organization during periods of reduced energy supply.
Thesis (Master, Biology) -- Queen's University, 2011-10-07 14:41:46.972

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39

Ko, Cheng-Wen y 柯正雲. "Implementation of Automatic Spike Detection System for EEG". Thesis, 1998. http://ndltd.ncl.edu.tw/handle/31092700983815263148.

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碩士
國立臺灣大學
電機工程學系研究所
86
An automatic spike detection algorithm for classification of multi-channelelec troencephalographic (EEG) signals based on artificial neural network is presen ted. Radial basis function (RBF) neural network was chosen for single channel recognition, with model optimization using receiver operating characteristics analysis. Waveform simplification was employed for high noise immunity. Fea ture extraction with as few as three parameters was used as preparation for th e inputs to the neural network. Identification of multi-channel geometric cor relation was performed to further lower the false-positive rate by using an in cidence matrix. Threshold value for spike classification was chosen for simul taneous maximization of detection sensitivity and selectivity. Evaluation wit h visual analysis in this preliminary study showed a 83% sensitivity using 16- channel continuous EEG records of four patients, while a high false positive r ate was found, which was believed to arise from the extensive andexhaustive vi sual analysis process. The computation time required for spike detection was significantly less than that needed for online display of the signals on the m onitor. We believe that the algorithm proposed in this study is robust and th at the simple structure of RBF neural network yields high potential for real-t ime implementation.

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40

Nabar, Nisseem S. "Wavelet Based Algorithms For Spike Detection In Micro Electrode Array Recordings". Thesis, 2008. http://hdl.handle.net/2005/745.

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In this work, the problem of detecting neuronal spikes or action potentials (AP) in noisy recordings from a Microelectrode Array (MEA) is investigated. In particular, the spike detection algorithms should be less complex and with low computational complexity so as to be amenable for real time applications. The use of the MEA is that it allows collection of extracellular signals from either a single unit or multiple (45) units within a small area. The noisy MEA recordings then undergo basic filtering, digitization and are presented to a computer for further processing. The challenge lies in using this data for detection of spikes from neuronal firings and extracting spatiotemporal patterns from the spike train which may allow control of a robotic limb or other neuroprosthetic device directly from the brain. The aim is to understand the spiking action of the neurons, and use this knowledge to devise efficient algorithms for Brain Machine Interfaces (BMIs). An effective BMI will require a realtime, computationally efficient implementation which can be carried out on a DSP board or FPGA system. The aim is to devise algorithms which can detect spikes and underlying spatio-temporal correlations having computational and time complexities to make a real time implementation feasible on a specialized DSP chip or an FPGA device. The time-frequency localization, multiresolution representation and analysis properties of wavelets make them suitable for analysing sharp transients and spikes in signals and distinguish them from noise resembling a transient or the spike. Three algorithms for the detection of spikes in low SNR MEA neuronal recordings are proposed: 1. A wavelet denoising method based on the Discrete Wavelet Transform (DWT) to suppress the noise power in the MEA signal or improve the SNR followed by standard thresholding techniques to detect the spikes from the denoised signal. 2. Directly thresholding the coefficients of the Stationary (Undecimated) Wavelet Transform (SWT) to detect the spikes. 3. Thresholding the output of a Teager Energy Operator (TEO) applied to the signal on the discrete wavelet decomposed signal resulting in a multiresolution TEO framework. The performance of the proposed three wavelet based algorithms in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families in the presence of colored AR(5) (autoregressive model with order 5) and additive white Gaussian noise (AWGN) is evaluated. The performance is further evaluated for the wavelet family chosen under different levels of SNR in the presence of the colored AR(5) and AWGN noise. Chapter 1 gives an introduction to the concept behind Brain Machine Interfaces (BMIs), an overview of their history, the current state-of-the-art and the trends for the future. It also describes the working of the Microelectrode Arrays (MEAs). The generation of a spike in a neuron, the proposed mechanism behind it and its modeling as an electrical circuit based on the Hodgkin-Huxley model is described. An overview of some of the algorithms that have been suggested for spike detection purposes whether in MEA recordings or Electroencephalographic (EEG) signals is given. Chapter 2 describes in brief the underlying ideas that lead us to the Wavelet Transform paradigm. An introduction to the Fourier Transform, the Short Time Fourier Transform (STFT) and the Time-Frequency Uncertainty Principle is provided. This is followed by a brief description of the Continuous Wavelet Transform and the Multiresolution Analysis (MRA) property of wavelets. The Discrete Wavelet Transform (DWT) and its ﬁlter bank implementation are described next. It is proposed to apply the wavelet denoising algorithm pioneered by Donoho, to first denoise the MEA recordings followed by standard thresholding technique for spike detection. Chapter 3 deals with the use of the Stationary or Undecimated Wavelet Transform (SWT) for spike detection. It brings out the differences between the DWT and the SWT. A brief discussion of the analysis of non-stationary time series using the SWT is presented. An algorithm for spike detection based on directly thresholding the SWT coefficients without any need for reconstructing the denoised signal followed by thresholding technique as in the first method is presented. In chapter 4 a spike detection method based on multiresolution Teager Energy Operator is discussed. The Teager Energy Operator (TEO) picks up localized spikes in signal energy and thus is directly used for spike detection in many applications including R wave detection in ECG and various (alpha, beta) rhythms in EEG. Some basic properties of the TEO are discussed followed by the need for a multiresolution approach to TEO and the methods existing in literature. The wavelet decomposition and the subsampled signal involved at each level naturally lends it to a multiresolution TEO framework at the same time significantly reducing the computational complexity due the subsampled signal at each level. A wavelet-TEO algorithm for spike detection with similar accuracies as the previous two algorithms is proposed. The method proposed here differs significantly from that in literature since wavelets are used instead of time domain processing. Chapter 5 describes the method of evaluation of the three algorithms proposed in the previous chapters. The spike templates are obtained from MEA recordings, resampled and normalized for use in spike trains simulated as Poisson processes. The noise is modeled as colored autoregressive (AR) of order 5, i.e AR(5), as well as Additive White Gaussian Noise (AWGN). The noise in most human and animal MEA recordings conforms to the autoregressive model with orders of around 5. The AWGN Noise model is used in most spike detection methods in the literature. The performance of the proposed three wavelet based algorithms is measured in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families. The optimal wavelet for this purpose is then chosen from the wavelet family which gives the best results. Also, optimal levels of decomposition and threshold factors are chosen while maintaining a balance between accuracy and false positives. The algorithms are then tested for performance under different levels of SNR with the noise modeled as AR(5) or AWGN. The proposed wavelet based algorithms exhibit a detection accuracy of approximately 90% at a low SNR of 2.35 dB with the false positives below 5%. This constitutes a significant improvement over the results in existing literature which claim an accuracy of 80% with false positives of nearly 10%. As the SNR increases, the detection accuracy increases to close to 100% and the false alarm rate falls to 0. Chapter 6 summarizes the work. A comparison is made between the three proposed algorithms in terms of detection accuracy and false positives. Directions in which future work may be carried out are suggested.

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41

Wang, Szu-Huai y 王思淮. "Spike Detection Based on Normalized Correlation with Automatic Template Generation". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/60902107400798368219.

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42

Chien, Chih Ting y 簡誌廷. "The Development of Spike Detection for Action Potential and Circuit Design". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/xpwwga.

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碩士
國立清華大學
電機工程學系
105
Because of the coming of aging society, the disease caused by neuron degeneration is more common than before. Because neuron degenerate, signal transmission between different neurons become abnormal. Patients will suffer from symptoms such as tremor, paresthesia and bradykinesia. The action potential signal is an indicator of neuronal activity. With detection and diagnosis of specific region neuronal activity in the same time, we can use stimulators to let the neuron cells normal. In recent year, some research teams focus on the development of Brain Machine Interface (BMI) including some different functions. The front-end unit consisting of both recording unit and stimulator can receive signal from and stimulate energy to neuron of the patient. By recording neuron signal from recording unit, the Arithmetic Logic unit (ALU) of the BMI system can give a diagnosis immediately and apply to the treatment of motor habitation or spinal cord injury. For immediate diagnosis and treatment, it should let the fronts end part implanted into the body of patient to prevent patient from infection. The device should operate at lower voltage to reduce power consumption. This work proposed in this thesis can receive the action potential signal of 0.01mV from neuron and use the algorithm to let spike signal transform to impulse waveform of 300mV with supply voltage of 1V. And this work can detect spike by single threshold voltage and can improve accuracy on spike detection to integrate to the BMI system beneficially. This work has been designed and fabricated with the TSMC 0.18μm process. The measurement results are presented and discussed in this thesis.

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43

Yung-ChunLiu y 劉勇均. "A Study on Spike Detection and Classification from Epileptic EEG Data". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/37413041782524463703.

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博士
國立成功大學
資訊工程學系
102
Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this thesis, a new two–stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is used to detect all possible spike candidates. Then, different kinds of features are extracted and applied to these candidates for spike classification. Moment descriptors are first applied as the features to describe the EEG candidate data and the empirical mode decomposed candidate data for spike classification. The statistical moments give promising classification results, however, the moment method does not include the shape information which is critical for epileptic spike classification. We subsequently propose a novel spike model-based method for spike classification. Although spikes with slow waves frequently occur in epileptic EEGs, they are not used in conventional spike detection. The newly proposed system accommodates both the single spike and spike with slow wave in the spike model. Using the AdaBoost classifier, the system outperforms the conventional spike model in both two- and three-class EEG classification problems. It not only achieves better accuracy in spike classification but provides new ability to differentiate between spikes and spikes with slow waves. Consequently, the proposed system has better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis.

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"On the Dynamics of Epileptic Spikes and Focus Localization in Temporal Lobe Epilepsy". Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.14811.

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abstract: Interictal spikes, together with seizures, have been recognized as the two hallmarks of epilepsy, a brain disorder that 1% of the world's population suffers from. Even though the presence of spikes in brain's electromagnetic activity has diagnostic value, their dynamics are still elusive. It was an objective of this dissertation to formulate a mathematical framework within which the dynamics of interictal spikes could be thoroughly investigated. A new epileptic spike detection algorithm was developed by employing data adaptive morphological filters. The performance of the spike detection algorithm was favorably compared with others in the literature. A novel spike spatial synchronization measure was developed and tested on coupled spiking neuron models. Application of this measure to individual epileptic spikes in EEG from patients with temporal lobe epilepsy revealed long-term trends of increase in synchronization between pairs of brain sites before seizures and desynchronization after seizures, in the same patient as well as across patients, thus supporting the hypothesis that seizures may occur to break (reset) the abnormal spike synchronization in the brain network. Furthermore, based on these results, a separate spatial analysis of spike rates was conducted that shed light onto conflicting results in the literature about variability of spike rate before and after seizure. The ability to automatically classify seizures into clinical and subclinical was a result of the above findings. A novel method for epileptogenic focus localization from interictal periods based on spike occurrences was also devised, combining concepts from graph theory, like eigenvector centrality, and the developed spike synchronization measure, and tested very favorably against the utilized gold rule in clinical practice for focus localization from seizures onset. Finally, in another application of resetting of brain dynamics at seizures, it was shown that it is possible to differentiate with a high accuracy between patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES). The above studies of spike dynamics have elucidated many unknown aspects of ictogenesis and it is expected to significantly contribute to further understanding of the basic mechanisms that lead to seizures, the diagnosis and treatment of epilepsy.
Dissertation/Thesis
Ph.D. Electrical Engineering 2012

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45

曾世平. "Automatic analysis and detection of EEG spikes". Thesis, 1991. http://ndltd.ncl.edu.tw/handle/23006107799247573232.

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46

Annau, Thomas Mark. "Models of visual feature detection and spike coding in the nervous system". Thesis, 1996. https://thesis.library.caltech.edu/3681/1/Annau_tm_1996.pdf.

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We propose mathematical models to analyze two nervous system phenomena. The first is a model of the development and function of simple cell receptive fields in mammalian primary visual cortex. The model assumes that images are composed of combinations of a limited set of specific visual features and that the goal of simple cells is to detect the presence or absence of these features. Based on a presumed statistical character of images and their visual features, the model uses a constrained Hebbian learning rule to discover the structure of the features, and thus the appropriate response properties of simple cells, by training on a database of photographs. The response properties of the model simple cells agree qualitatively with neurophysiological observation. The second is a model of the coding of information in the nervous system by the rate of axonal voltage spikes. Assuming an integrate-and-fire mechanism for spike generation, we develop a quantization-based model of rate coding and use it to derive the mathematical relationship between the amplitude and temporal resolution of a rate encoded signal. We elaborate the model to include integrator leak in the spike generation mechanism and show that it compactly combines coding and the computation of a threshold function.

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47

Kuo, Chen-Wei y 郭鎮瑋. "Dendritic Spine Detection and Registration of Neuron Images". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/26252239210178245842.

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碩士
國立成功大學
電腦與通信工程研究所
97
In this paper, we present a method to automatically detect the dendritic spines and register time-lapse images of dendrite for facilitate investigation of neural functions. To segment dendritic spines, tone reproduction is adopted. The Delaunay triangulation based centerline extraction is used to extract dendritic backbones. The dendritic spines are detected according to the space relationship between backbones and spines. We employ iterative closest point method to register the time-lapse data. Comparison results are also made for both static and time-series data between the proposed and manual results.

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Shridharani, Jay Ketan. "Injury Detection and Localization in the Spine using Acoustic Emission". Diss., 2016. http://hdl.handle.net/10161/13427.

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The National Spinal Cord Injury Statistical Center estimates there are 12,500 new cases of spinal cord injury (SCI) in the United States every year (www.nscisc.uab.edu, 2014) and vehicular crashes are the leading cause. Spinal injuries can have extensive long term consequences leading to widespread social and economic costs as well as the human cost of living with chronic, sometimes debilitating, pain (Côté et al. 1998, Côté et al. 2001, Daffner et al. 2003, Harrop et al. 2001, Sekhon et al. 2001). Within the military population, spinal injuries are a common result of repeated loading from high-speed planing watercraft (Bass et al. 2005, Gollwitzer et al. 1995, Schmidt et al. 2012), high performance aircraft (Coakwell et al. 2004, de Oliviera et al. 2005), and underbody blast exposure (Vasquez et al. 2011, Wilson 2006). Therefore, there is interest within the automotive, military, and clinical communities to understand the biomechanics the failure mechanics of the osteoligamentous structures in the spine.

Acoustic emissions have been shown to be produced during micro-cracking of cortical bone (Kohn 1995). However, there has been minimal work utilizing acoustic emission to detect cortical and trabecular bone damage. The research in this dissertation developed experimental and analytic methods of sensitively assessing when failure (both micro-cracks and more extensive failures) occurs in the cervical spine using acoustic emissions.

The acoustic emissions from cortical and trabecular bone failure were characterized using a Welch power spectrum density estimate and continuous wavelet transform. The power spectrum density results showed both cortical bone and trabecular bone failure produced wideband acoustic emission signals with spectral peaks between from 20 kHz to 1380 kHz and 24 kHz to 1382 kHz respectively. The continuous wavelet transform showed the spectral content begins with high frequency content followed quickly by low frequency content, but the low frequency lasts for a longer time causing it to dominate the response in the Welch power spectrum density. The first frequency component in the continuous wavelet transform was used to characterize the signals and was found to form three distinct bands in the cortical bone tests (166 ± 52.6 kHz, 379 ± 37.2 kHz, and 668 ± 63.4 kHz) and one band in the trabecular bone tests (185 ± 37.9 kHz). Therefore, observing spectral content within these bands suggests failure of the respective bone.

This dissertation also uses continuous wavelet transform to identify failure in whole cervical spine compression tests. Whole cervical spines placed in a pre-flexed and pre-extended posture were compressed to induce failure while being monitored for acoustic emissions. Cortical bone failure was identified in the acoustic emissions when local maxima in the continuous wavelet transform fell within the spectral bands associated with cortical bone failure previously identified. The timing of these failures was matched to the force-displacement response to identify the initiation of failure and the major failure. Cortical bone failure was detected at 70-90% of the failure load suggesting that the failure occurs as an evolution from micro-cracks to the eventual major failure. Locating these micro-cracks before the major failure forms may be useful in the prediction of the location of failure.

This dissertation also presents a technique to calculate the AE source location for AEs generated from fracture. The primary obstacle for AE source localization in the spine is that the speed of sound is different in cortical bone (Prevrhal et al. 2001), trabecular bone (Cardoso et al. 2003), intervertebral disc (Pluijm et al. 2004), ligaments (Kijima et al. 2009), and also differs based on its direction of travel in cortical bone (Kann et al. 1993) and likely in the other materials. Any algorithm must account for these differences to obtain any useful level of accuracy. The algorithm presented in this dissertation is based on hyperbolic source location algorithms (De Ronde et al. 2007, O'Toole et al. 2012, Salinas et al. 2010) except that it iterates on the speed of sound over a specified range, and convergence is defined as when the solution change is minimized. This procedure calculated the AE source location with a mean error of 5.7 mm and a standard deviation of 3.8 mm.

The contributions and conclusions of this dissertation provide methodology and results to evaluate the failure mechanics in the spine. Although these procedures were developed for use in the spine, they are of great value to the biomechanics community because they are applicable to every body region. The recommendations presented will serve to better understand the failure mechanics of the human body and will likely lead to better defined and safer standards for protective equipment. It also provides data for the generation of finite element models that require failure criteria.

Dissertation

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49

Chen, Zih-Ying y 陳姿穎. "Fe3O4@Ag nanoparticles for surface enhanced Raman spectroscopic detection of Hg2+ in spiked cosmetics". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/bnskyv.

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碩士
國立陽明大學
生醫光電研究所
107
Surface enhanced Raman spectroscopy (SERS) has been used for the detection of mercury ions (Hg2+) in spiked cosmetic (skin whitening) samples by using Fe3O4@Ag-DMcT nanoparticles (NPs). Silver (Ag) coated iron oxide (Fe3O4) NPs (12±4 nm) are the magneto-plasmonic SERS enhancers, and the 2,5-Dimercapto-1,3,4-thiadiazole (DMcT) 1360 cm-1 band as the Hg2+ reporter. The elemental composition was confirmed by energy dispersive analysis of X-ray (EDAX) mapping, and the functionalization with DMcT was confirmed by UV-Vis spectroscopy. We have optimized 2 mg/ml of Fe3O4@Ag:DMcT with 10-4 M of DMcT to be the best SERS enhancer in our study that we did in the solution phase inside a sealed capillary tube placed on a magnet. A calibration curve of the variation of the 1360 cm-1 band as a function of Hg2+ concentration was first determined. We did the recovery study in the spiked samples (commercial skin whitening product) and obtained the accuracy of intensity value of 81.1±35.3%, and 91.7±14.6% in low (0.00000001 M), and high (0.0001 M) spike concentrations, respectively, estimated from the calibration curve. We estimate a limit of detection (LoD) for Hg2+ in real cosmetic sample as 0.00000001 M (2 ppb).

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Decker, Colleen. "The Effect of Skin and Soft Tissue on Spinal Frequency Response Measurements". Master's thesis, 2010. http://hdl.handle.net/10048/1313.

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Introduction: This study sought to investigate the effects of soft tissue on measurements of a spinal vibration response using skin-mounted accelerometers and a non-invasive contact tip. Methods: Vibration was applied to the spine of porcine and human cadavers. Measurements of the spinal vibration response were taken from needle, skin, and bone-mounted accelerometers. Several skin-mounted accelerometer placements dorsal to a spinous process were tested, and 6 different non-invasive contact tip shapes were used to explore sources of variance in the signals. Results: Vibration measured from skin-mounted accelerometers had altered signal patterns compared to bone-mounted accelerometers. The measured FRF was found to be sensitive to accelerometer positioning. No significant difference in skin-bone correlation was attributed to contact tip shape or vertebral level. Conclusion: The use of a non-invasive contact tip excites vibration in the soft tissues which overlay the spine, in addition to the vertebral column. This vibration interferes with skin sensor measurements of vertebral vibration response, with the effect diminishing as distance from the contact tip increases. Small changes in contact tip shape do not affect the correlation between skin and bone signals.

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