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Alam, M. (Md ). "Automatic ECG signal quality assessment." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906052442.
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Abstract. The quality assessment of signal has been a research topic for many years, as it is mainly related to the problem of the false alarms. Automatic quality detection/assessment and classification of signals can play a vital role in the development of robust unsupervised electrocardiogram (ECG). The development of efficient algorithms for the quality control of ECG recordings is essential to improve healthcare now. ECG signal can be intermixed with many kinds of unwanted noises. It is an important task to assess the quality of the ECG signal for further biomedical inspections. To make that happen, we made an algorithm that is efficient and uses some basic quality features to classify the ECG signals. It is a very effective way to acquire a good quality ECG signal in real-time by unskilled personnel for instance in rural areas there is not enough expertise in this field. By using this method, they can quickly know if the ECG signal is acceptable or unacceptable for further inspections. The method is used to assess the quality of the ECG signals in the training set of the Physionet/Computing in Cardiology Challenge 2011, giving a correct interpretation of the quality of the ECG signals of 93.08% which corresponded to a sensitivity of 96.53% and a specificity of 86.76%.
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Wang, Limin. "The ECG signal processing by ADSP-21062 digital signal processor." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=840.
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Thesis (M.S.)--West Virginia University, 1999.Title from document title page. Document formatted into pages; contains vi, 110 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 66-68).
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Taji, Bahareh. "Signal Quality Assessment in Wearable ECG Devices." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38851.
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There is a current trend towards the use of wearable biomedical devices for the purpose of recording various biosignals, such as electrocardiograms (ECG). Wearable devices have different issues and challenges compared to nonwearable ones, including motion artifacts and contact characteristics related to body-conforming materials. Due to this susceptibility to noise and artifacts, signals acquired from wearable devices may lead to incorrect interpretations, including false alarms and misdiagnoses. This research addresses two challenges of wearable devices. First, it investigates the effect of applied pressure on biopotential electrodes that are in contact with the skin. The pressure affects skin–electrode impedance, which
impacts the quality of the acquired signal. We propose a setup for measuring skin–electrode impedance during a sequence of applied calibrated pressures. The Cole–Cole impedance model is utilized to model the skin–electrode interface. Model parameters are extracted and compared in each state of measurement with respect to
the amount of pressure applied. The results indicate that there is a large change in the magnitude of skin–electrode impedance when the pressure is applied for the first time, and slight changes in impedance are observed with successive application and release of pressure. Second, this research assesses the quality of ECG signals to reduce issues related to poor-quality signals, such as false alarms. We design an algorithm based on Deep Belief Networks (DBN) to distinguish clean from
contaminated ECGs and validate it by applying real clean ECG signals taken from the MIT-BIH arrhythmia database of Physionet and contaminated signals with motion artifacts at varying signal-to-noise ratios (SNR). The results demonstrate that the algorithm can recognize clean from contaminated signals with an accuracy of
99.5% for signals with an SNR of -10 dB. Once low- and high-quality signals are separated, low-quality signals can undergo additional pre-processing to mitigate the contaminants, or they can simply be discarded. This approach is applied to reduce the false alarms caused by poor-quality ECG signals in atrial fibrillation (AFib)
detection algorithms. We propose a signal quality gating system based on DBN and validate it with AFib signals taken from the MIT-BIH Atrial Fibrillation database of Physionet. Without gating, the AFib detection accuracy was 87% for clean ECGs, but it markedly decreased as the SNR decreased, with an accuracy of 58.7% at an SNR
of -20 dB. With signal quality gating, the accuracy remained high for clean ECGs (87%) and increased for low SNR signals (81% for an SNR of -20 dB). Furthermore, since the desired level of quality is application dependent, we design a DBN-based algorithm to quantify the quality of ECG signals. Real ECG signals with various types of arrhythmias, contaminated with motion artifacts at several SNR levels, are thereby classified based on their SNRs. The results show that our algorithm can perform a multi-class classification with an accuracy of 99.4% for signals with an SNR of -20 dB and an accuracy of 91.2% for signals with an SNR of 10 dB.
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Ravuru, Anusha. "Characterization of Ecg Signal Using Programmable System on Chip." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc177242/.
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Electrocardiography (ECG) monitor is a medical device for recording the electrical activities of the heart using electrodes placed on the body. There are many ECG monitors in the market but it is essential to find the accuracy with which they generate results. Accuracy depends on the processing of the ECG signal which contains several noises and the algorithms used for detecting peaks. Based on these peaks the abnormality in the functioning of the heart can be estimated. Hence this thesis characterizes the ECG signal which helps to detect the abnormalities and determine the accuracy of the system.
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Fachrudin, Imam. "Traitement du signal ECG - approche par la transformation en ondelettes." Rouen, 1995. http://www.theses.fr/1995ROUES040.
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Les travaux présentés dans ce mémoire ont pour objectif d'évaluer l'efficacité de la transformation en ondelettes (TO) dans les opérations de traitement du signal ECG : l'analyse, la détection, le filtrage et la compression. L'analyse temps-fréquence par la TO permet de mettre en évidence les structures fréquentielles des composantes ECG : ses ondes, ses bruits et certains événements pathologiques. Les algorithmes de détection de complexes ECG sont élaborés à partir des propriétés du module et de la phase de la transformation. La relation de la TO avec l'analyse multirésolution et la découverte de l'algorithme rapide de la TO sont les bases du développement de la méthode de filtrage et du nouveau concept de compression de données. Les performances de chacune des méthodes sont comparables à celles des méthodes existantes, sinon meilleures
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Poiseau, Éric. "Traitement du signal appliqué à l'analyse des signaux EEG et ECG enregistrés pendant le sommeil." Compiègne, 1993. http://www.theses.fr/1993COMPD592.
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Ce mémoire concerne l'analyse des signaux EEG et ECG enregistrés pendant le sommeil en vue de l'identification automatique des différents stades du sommeil et de l'évaluation quantitative de l'effet de certaines drogues sur les paramètres électrophysiologiques les caractérisant. Le premier chapitre est un chapitre d'introduction qui présente les différents stades du sommeil. Il présente les conditions expérimentales d'acquisition des signaux utilisés pour l'évaluation des algorithmes décrits dans les chapitres suivants. Le second chapitre est consacré à l'exposé de méthodes d'analyse des signaux EEG durant le stade 2 du sommeil. Deux approches complémentaires ont été développées et validées. La première repose sur une technique de filtrage adapté. Le signal EEGB est convolué avec des "templates" simulant des formes types de fuseaux de sommeil et de complexes-K. La détection repose sur un algorithme multicritères mettant en jeu des seuils d'amplitude de durée et d'énergie. La deuxième approche utilise une analyse spectrale multi-canaux afin d'étudier la répartition énergétique de l'EEG à la surface du scalp en vue de la localisation de l'activité fuseau. Ces deux analyses mettent en évidence la présence de fuseaux de sommeil pendant les stades 3 et 4 du sommeil. Le troisième chapitre traite de l'étude de la variabilité du rythme cardiaque durant les différents stades du sommeil. Un algorithme pour la détection automatique des complexes QRS est proposé. Le processus de reconnaissance retenu repose sur l'étude des pseudo-périodicités morphologiques au sein du signal ECG. Il combine analyse morphologique et approche physiologique. L'algorithme a été utilisé pour l'étude des variations de l'intervalle R-R en fonction des différents stades du sommeil et de l'effet de substances pharmacologiques. Il est montré que certaines variations de l'intervalle R-R pourraient être utilisées pour prévoir les changements dans l'architecture du sommeil.
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Bracková, Michaela. "Hodnocení kvality signálů EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401000.
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This diploma thesis deals with the topic of the ECG quality evaluation. The theoretical part of the thesis contains an overview of the methods, which were studied and an explanation of the basic principles connected with the quality evaluation of the ECG signals. The practical part deals with the implementation of three selected methods, one of which is the continuous evaluation of signal quality by means of SNR (signal to noise ratio) calculation. The results of these methods are further discussed and compared.
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Peddaneni, Hemanth. "Comparison of algorithms for fetal ECG extraction." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0007480.
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Rattfält, Linda. "Smartware electrodes for ECG measurements : Design, evaluation and signal processing." Doctoral thesis, Linköpings universitet, Fysiologisk mätteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-100134.
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The aim of this thesis work has been to study textile and screen printed smartware electrodes for electrocardiographic (ECG) measurements both in terms of their electrode properties and possibility to further improve their robustness to movement induced noise by using signal processing. Smartware electrodes for ECG measurements have previously been used in various applications but basic electrical electrode properties have not sufficiently been looked into. Furthermore, we believe that there is a possibility to reduce disturbances in the smartware ECG by adding redundant sensors and applying sensor fusion signal processing. Electrical properties of conductive textiles have been evaluated in terms of stability and electrode impedance. Three yarns and textile electrode surfaces were tested. The electrodes made from pure stainless steel and 50\% stainless steel/ 50\% polyester showed acceptable stability of electrode potentials. All electrode measurements were performed on skin. Furthermore, we produced six screen printed electrodes and their electrical performance was investigated in an electrochemical cell. The tested inks contained carbon or silver particles in the conduction lines, and Ag/AgCl particles in the electrode surface. Results show that all electrodes were stable in time, with a maximum drift of a few mV during 30 minutes. The silver ink is superior to the carbon based in terms of electrode impedance at the higher frequencies. To extract viable physiological information from noisy signals, canonical correlation analysis (CCA) was applied on multi-channel ECG signals recorded with textile electrodes. Using CCA to solve the blind source separation (BSS) problem, we intended to separate the ECG signal from the various noise sources. The method (CCABSS) was compared to averaging of the ECG channels and to the independent component analysis method (ICA). In the dataset consisting of noisy ECG recordings, the signal was uninterpretable in 7% after CCABSS. Corresponding values for averaging and ICA were 33% and 17%, respectively. Smartware applications often include heartbeat detection while moving, a measurement situation which is prone to produce noise corrupted ECG signals. To compensate for this, we used an event detector based on a multi-channel input, a model of the event and weighted correlation. For measurements at rest and static muscle tension, the sensitivity of the event detector was 97% and 77% respectively. Corresponding values for the golden standard detector Pan-Tompkins were 96% and 52%, respectively.
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Sarfraz, M. "Role of independent component analysis in intelligent ECG signal processing." Thesis, University of Salford, 2014. http://usir.salford.ac.uk/33200/.
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The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and reveals very important hidden information in its structure. The information is extracted by means of ECG signal analysis to gain insights that are very crucial in explaining and identifying various pathological conditions. The feature extraction process can be accomplished directly by an expert through, visual inspection of ECGs printed on paper or displayed on a screen. However, the complexity and the time taken for the ECG signals to be visually inspected and manually analysed means that it‟s a very tedious task thus yielding limited descriptions. In addition, a manual ECG analysis is always prone to errors: human oversights. Moreover ECG signal processing has become a prevalent and effective tool for research and clinical practices. A typical computer based ECG analysis system includes a signal preprocessing, beats detection and feature extraction stages, followed by classification. Automatic identification of arrhythmias from the ECG is one important biomedical application of pattern recognition. This thesis focuses on ECG signal processing using Independent Component Analysis (ICA), which has received increasing attention as a signal conditioning and feature extraction technique for biomedical application. Long term ECG monitoring is often required to reliably identify the arrhythmia. Motion induced artefacts are particularly common in ambulatory and Holter recordings, which are difficult to remove with conventional filters due to their similarity to the shape of ectopic xiii beats. Feature selection has always been an important step towards more accurate, reliable and speedy pattern recognition. Better feature spaces are also sought after in ECG pattern recognition applications. Two new algorithms are proposed, developed and validated in this thesis, one for removing non-trivial noises in ECGs using the ICA and the other deploys the ICA extracted features to improve recognition of arrhythmias. Firstly, independent component analysis has been studied and found effective in this PhD project to separate out motion induced artefacts in ECGs, the independent component corresponding to noise is then removed from the ECG according to kurtosis and correlation measurement. The second algorithm has been developed for ECG feature extraction, in which the independent component analysis has been used to obtain a set of features, or basis functions of the ECG signals generated hypothetically by different parts of the heart during the normal and arrhythmic cardiac cycle. ECGs are then classified based on the basis functions along with other time domain features. The selection of the appropriate feature set for classifier has been found important for better performance and quicker response. Artificial neural networks based pattern recognition engines are used to perform final classification to measure the performance of ICA extracted features and effectiveness of the ICA based artefacts reduction algorithm. The motion artefacts are effectively removed from the ECG signal which is shown by beat detection on noisy and cleaned ECG signals after ICA processing. Using the ICA extracted feature sets classification of ECG arrhythmia into eight classes with fewer independent components and very high classification accuracy is achieved.
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Gupta, Aditya. "SIGNAL PROCESSING OF AN ECG SIGNAL IN THE PRESENCE OF A STRONG STATIC MAGNETIC FIELD." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2206.
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This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal.Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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NIBHANUPUDI, SWATHI. "SIGNAL DENOISING USING WAVELETS." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1070577417.
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Malý, Tomáš. "Automatické rozpoznání kvality signálů EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413167.
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This thesis deals with issues of automatic quality estimation of ECG signals. The main aim of this thesis is to implement own algorithm for classifying ECG signals into three classes of quality. Theoretical part of the thesis contains mostly description of recording electrical activity of the heart, anatomy and physiology of the heart, electrocardiography, different types of ECG signals interference and two of the chosen methods for quality estimation. Implementation of the chosen methods is presented in the practical part. Result of this thesis are two implemented algorithms, which are based on methods described in the theoretical part. The first of two is based on detection of R-waves, validation of physiological assumptions and the subsequent calculation of the correlation coefficient between adaptive template and interfered signal. Second is based on calculation of a continuous SNR value over time, which is then thresholded. The robustness of the methods was verified on the three specified real ECG signals, which are all available on UBMI including annotation of specific signal parts. Those 24-hour long signals were recorded by Holter monitor, which is described in the theoretical part of the thesis. Achieved results of individual methods, including their comparison with annotation and statistical evaluation are presented in the conclusion of this thesis.
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Taji, Bahareh. "Reconstruction of ECG Signals Acquired with Conductive Textile Eletrodes." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/26303.
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Physicians’ understanding of bio-signals, measured using medical instruments, becomes the foundation of their decisions and diagnoses of patients, as they rely strongly on what the instruments show. Thus, it is critical and very important to ensure that the instruments’ readings exactly reflect what is happening in the patient’s body so that the detected signal is the real one or at least as close to the real in-body signal as possible and carries all of the appropriate information. This is such an important issue that sometimes physicians use invasive measurements in order to obtain the real bio-signal. Generating an in-body signal from what a measurement device shows is called “signal purification” or “reconstruction,” and can be done only when we have adequate information about the interface between the body and the monitoring device. In this research, first, we present a device that we developed for electrocardiogram (ECG) acquisition and transfer to PC. In order to evaluate the performance of the device, we use it to measure ECG and apply conductive textile as our ECG electrode. Then, we evaluate ECG signals captured by different electrodes, specifically traditional gel Ag/AgCl and dry golden plate electrodes, and compare the results. Next, we propose a method to reconstruct the ECG signal from the signal we detected with our device with respect to the interface characteristics and their relation to the detected ECG. The interface in this study is the skin-electrode interface for conductive textiles. In the last stage of this work, we explore the effects of pressure on skin-electrode interface impedance and its parametrical variation.
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Janjarasjitt, Suparerk. "A NEW QRS DETECTION AND ECG SIGNAL EXTRACTION TECHNIQUE FOR FETAL MONITORING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1144263231.
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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.
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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.
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Real, Luiz Fernando Oliveira Corte. "Codificação e compressão iterativa de sinais biomédicos." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-20082013-155137/.
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Em Biomedicina, a detecção e a quanticação de anormalidades presentes num sinal são desejáveis. Uma estratégia de codicação baseada em extração de características, tais como picos ou frequências, pode não capturar todas as irregularidades. Assim, uma representação baseada em funções de base denidas com conhecimento a priori do sinal pode ser mais precisa para aplicações biomédicas. A escolha das funções base depende da natureza siológica do sinal e de suas peculiaridades. Sinais de eletrocardiograma (ECG) e eletroencefalograma (EEG) exibem características bem denidas. ECG, por exemplo, é um sinal elétrico composto de uma forma de onda especíca (P, QRS e T). Se as características de um sinal a ser sintetizado são bem compreendidas, é possível derivar uma assinatura para o sinal. Uma codicação apropriada permite a extração de parâmetros relevantes para sua análise, tais como anormalidades num ciclo cardíaco representadas por uma alteração no sinal de ECG, ou então uma excitação das ondas cerebrais representada por uma modicação no sinal de EEG. O objetivo deste projeto é introduzir uma nova técnica de codicação de sinais, que representa um sinal pela soma de funções sigmoides para aproximar iterativamente o sinal medido, com foco em aplicações biomédicas. Funções sigmoides tendem a reproduzir bem as grandes variações presentes em sinais biomédicos, daí a escolha de usá-las na codicação deste tipo de sinal. Serão explorados o nível de compressão dos dados, bem como a taxa de convergência. A técnica desenvolvida será comparada com técnicas convencionais de codicação e sua robustez será avaliada. Uma estratégia de codicação ótima pode trazer benefícios não só para a compressão, mas também na criação de assinaturas de sinais representando tanto condições siológicas normais como patológicas.In Biomedicine, detection and quantication of abnormalities present in a signal are desired. An encoding strategy based on feature extraction, such as peaks or frequencies, may not capture all irregularities. Thus, a function-based representation, constructed using a priori knowledge of signal characteristics, may be more accurate for biomedical applications. The choice of the basis function depends on the physiological nature of the signal and its specic features. Electrocardiogram (ECG) and electroencephalogram (EEG) signals exhibit well-dened characteristics. ECG, for instance, is an electrical signal composed of specic waveform (P, QRS, and T). If the characteristics of a signal to be synthesized are well understood, its possible to derive a signal signature. An appropriate encoding allows the extraction of parameters relevant for its analysis, such as, abnormalities in a cardiac cycle represented by an alteration in the ECG signal, or an excitation of the brain waves represented by a modication of the EEG. The objective of this project is to introduce a novel signal encoding technique that represents a signal by a sum of sigmoidal functions to iteratively approximate the measured signal, targeted at biomedical applications. Sigmoidal functions tend to reproduce well large variations in biomedical signals, hence their use for coding this type of signal. We explore the data compression level as well as the convergence rate. We also compare it to conventional encoding techniques and assess the robustness of this model. An optimal encoding strategy may bring not only benets in compression, but also in the creation of signatures for signals representing both physiological and pathological conditions.
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Stantic, Dejan. "Abnormality Detection from ECG Signals Using Multiscale Wavelet Analysis." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/367263.
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Cardiovascular disease is the number one cause of mortality in the western world, responsible for more than 16 million deaths annually worldwide. It can be diagnosed from Electro Cardiogram (ECG) signal, which captures the cardiac activities of the heart. Such signal apart of a large volume and velocity is often characterised by low quality due to the noise and other artefacts. In order to extract features from the ECG signal and perform the classication preprocessing, noise removal is required and it plays an important role in correct feature extraction and classication. Despite the signicant attention devoted in literature to preprocessing, feature extraction and classication of ECG signal, the accuracy is still the main concern, which could be one of the reasons why medical practitioners have not yet accepted system recommendations in their diagnosis. This is clear indication that the additional work is required.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
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Khawaja, Antoun. "Automatic ECG analysis using principal component analysis and wavelet transformation." Karlsruhe Univ.-Verl. Karlsruhe, 2007. http://www.uvka.de/univerlag/volltexte/2007/227/.
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Lin, Pei-Feng, and 林佩芬. "Correlation analysis between ECG and EEG signals based on signal complexity." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/05363265560635320629.
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博士國立臺灣大學
生醫電子與資訊學研究所
103
Introduction The secret of life remains extremely concealed. There are all sorts of rhythms in human bodies and they are central to life. The rhythms interact with each other as well as the outside fluctuating, noisy environment under the control of innumerable feedback systems. They provide an orderly function that enables life. The heart has been considered the source of emotional experience and wisdom in many cultures throughout the ages. Most neuroscientists consider consciousness or even thought is merely an epiphenomenon of the human brain function and its associated neurophysiology. However, the heart begins to beat before the brain is formed. Conventionally, both neural and humoral pathways connect the heart with the brain. Whether the interplay between the heart and brain could be explored through their rhythms is the question. Heart rate variability is recognized as the indicator of cardiac autonomic function. The dynamics of human electroencephalography (EEG) dynamics has been proved to be related to cognitive activities. This dissertation starts with reviewing the nonlinear methods in analyzing biological rhythms, which are multiscale, nonlinear and non-stationary. Regardless of whether chaos is present, deterministic complexity exists in biological rhythms. Regularity based complexity was chosen after comparisons. The goal is to find correlations between EEG and electrocardiography (ECG) through regularity based complexity analysis. Both simultaneous and non-simultaneous data would be examined. The experimental subjects are from a geriatric sample with varied cognitive abilities and basically healthy hearts. The electromagnetic activity of the brain works at an extremely fast speed, and the quasi-stationary epochs of EEG are, in general, short lasting, in the order of tens of seconds. Therefore symbolic techniques were introduced when exploring the very short simultaneous EEG and R-R interval (RRI) data. The origin of EEG remains unknown. Slow cortical potential (SCP), one component of EEG, is in the frequency range similar to that of the heart, and would be explored in an intuitive nonlinear way. In addition, the amplitude and instantaneous frequency of EEG would be separately approached. Methods The sample consisted of 89 geriatric outpatients in three patient groups: 38 fresh cases of vascular dementia (VD), 22 fresh cases of Alzheimer’s disease (AD) and 29 controls. Multiscale entropy (MSE) analysis was applied to the non-simultaneous EEG and RRI data. Symbolic analysis was applied to the simultaneous EEG and RRI data. Discrete events (local peaks) of EEG were extracted to separate the amplitude and instantaneous frequency. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. Results and Discussions MSE revealed correlations between the signal complexity of brain and cardiac activities in non-simultaneous data. Linear correlation between the MSE value and the score of the mini-mental state examination was first found. Symbolic dynamics failed to correlate the heart to the brain. This is due to that the RRI is too short to represent the characteristics of a subject. The symbolic analysis revealed important information that the EEG dynamics which relates to either the cognitive functions or the underlying pathologies of dementia are embedded within the dynamics of the amount of but not the interval between each synchronized firing of adjacent cerebral neurons. Just like RRI of ECG, discrete events of EEG also provided important information. The relative value of complexity does not indicate health condition straightly. It depends on the method and the scale or dimension that particular method measures. Discrete events provide no less information than continuous waveforms of EEG. Pathological condition is continuous rather than stepwise.
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Chiu, Shao-Yu, and 邱少禹. "Extraction of ECG, EGG and respiratory signal from single composite abdominal signal." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/60950518097141706285.
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碩士國立臺灣大學
醫學工程學研究所
97
The lack of integrated bio-signal detection instruments made monitor patients’ multiple physiology parameters rather complicated in the past. Many electrodes need be applied to the body surface at the same time. Those recording devices may have interference with by each other. In addition, patients at home may have sudden attack of discomfort, an easy implemented device that can record a variety of essential physiological signals through simple operation will be extremely helpful. These signals can also be transferred through the network to health care specialists. For above purposes, we implemented a portable device using few electrodes on abdominal wall to measure various patients’ electrophysiology signals simultaneously. The signals were acquisited through three electrodes placed on abdomen wall and were separated into Electrocardiogram (ECG), Electrogastrogram (EGG) and respiratory rhythm according to their individual rhythmic characters. In this thesis, it set up a combinatory ECG, EGG and respiratory signal system which includes the hardware for data acquisition and storage. In ECG signal processing, dynamic window with the baseline wandering fitting algorithm was noted to solve the drifting problem caused by respiration. The validation of our combinatory monitoring system was verified by synchronous recording using commercial available individual system. Good ECG correlation was demonstrated in 17 subjects in a long duration (1 hour) or short time (5 minutes) analysis. In EGG signal processing, a special designed electrode was used to ensure simultaneously recording. In a 10 subjects study, a long duration (1 hour) or short time(20 minutes) analysis are both show good correlation. The respiratory signal component was verified by twice down-sampling processing and the usage of twice filtering. A good respiratory signal correlation was demonstrated in 10 subjects. In brief. We had set up a system which can accurately record three sets of physiological signals with three electrodes on upper abdomen. High frequency high amplitude ECG signals and low frequency low amplitude ECG signals in accompany with respiratory movement signal can be simultaneously recorded. The mixed tracing can then be separated according to their characteristics. This simple design is very user friendly and can be applied to ambulatory physiological monitoring especially for the purpose of symptom correlation.
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Panigrahy, Damodar. "Extraction of Fetal ECG Signal from the Single Channel Abdominal ECG Signal Recording." Thesis, 2018. http://ethesis.nitrkl.ac.in/9793/1/2018_PHD_DPanigrahy_513EE1007_Extraction.pdf.
Full textAbstract:
The popular technique used for detection of fetal heart rate before delivery is Fetal
Electrocardiogram (FECG). It shows the muscular function and electrical activity of the
fetus heart. It represents the characteristics such as dynamic behaviors, waveform and
heart rate of the fetus. These characteristics help to determine the fetal development,
the existence of fetal distress, fetal life, fetal maturity or congenital heart disease. These
above characteristics help the doctors for appropriate treatment during pregnancy. The
heart rate of the fetus can easily be detected after estimation of the fetal ECG signal
from the abdominal ECG signal. The abdominal ECG signal is collected by placing
electrode at the abdomen area of the mother. The abdominal ECG signal contains fetal
ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal
from the abdominal ECG signal, removal of the noise and the maternal ECG component
presented in it is very much necessary.
Maternal ECG component is the dominant part of the abdominal ECG signal. To
remove the maternal ECG component present in the abdominal ECG signal, accurate
detection of the maternal R peaks from the abdominal ECG signal is required. An
efficient R peak detection technique is required for the detection of accurate maternal
R peaks from the abdominal ECG signal and the accurate detection of R peaks of the
extracted fetal ECG signal as well. So that heart rate of the fetus can be computed.
However, almost all existing R peak detectors suffer due to the non-stationary behavior
of both QRS morphology and noise. To overcome these difficulties, we have proposed
a three-stage improved method to detect R peaks based on Shannon energy envelope.
The proposed R peak detection method in this dissertation shows improved performance
compared to other existing methods available in the literature.
In the recent years, Extended Kalman Smoother (EKS) has been used and has
shown good performance for extraction of the fetal ECG signal from the single channel
abdominal ECG signal. But the limitation of this method is that it fails to extract fetal
QRS complex if it is overlapped by the maternal QRS complex in the abdominal ECG
signal. The method also sometimes requires operator’s interaction to initialize the
parameter of EKS to extract the fetal ECG signal which is dependent on abdominal
ECG signal for better performance. Author of this thesis has investigated the
effectiveness of Adaptive Neuro-Fuzzy Inference System (ANFIS) with EKS for
extraction of the fetal ECG signal using single channel abdominal ECG signal. The
EKS with ANFIS method proposed in this work for fetal ECG extraction is found to
detect fetal QRS complex even if it is overlapped by the maternal QRS complex in the
abdominal ECG signal. In the EKS with ANFIS framework, proposed Shannon energy
based R peak detection is used for detection of the maternal R peaks from the
abdominal ECG signal.
The EKS filtering framework for denoising purpose requires operator’s interaction.
To avoid the operator’s interaction and also to provide better performance using EKS
framework, author has investigated the effectiveness of the Extended Kalman Smoother
(EKS) with the Differential Evolution (DE) technique for noise cancellation of the ECG
signal. DE is used as an automatic parameter selection method for the selection of
10 optimized parameters of the ECG signal, and these are used to create the ECG
signal according to the real ECG signal. Also, these parameters are used in the EKS
algorithm for the development of the state equation and initialization of the parameters
of the EKS. The EKS framework is used for denoising of the ECG signal from the single
channel recording. The effectiveness of the proposed noise cancellation technique has
been evaluated by adding white, colored Gaussian noise and real muscle artifact noise
at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia
database. The proposed noise cancellation technique of ECG signal shows better Signal
to Noise Ratio (SNR) improvement, lesser Mean Square Error (MSE) and Percent of
Root mean square Distortion (PRD) compared to other well-known methods.
Finally, the author has proposed a five-stage based methodology for further
improvement of extracted FECG from the single channel abdominal ECG using DE
algorithm, EKS and ANFIS framework. The pre-processing stage is used to remove the
noise from the abdominal ECG signal and the EKS framework is used to estimate the
maternal ECG signal from the abdominal ECG signal. The optimized parameters of
the maternal ECG component (signal) are required to develop the state and
measurement equation of the EKS framework and the same are selected by the DE
algorithm. The relationship between the maternal ECG signal and the available
maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the
actual maternal ECG component present in the abdominal ECG signal and also to
recognize this nonlinear relationship, the ANFIS is used. Inputs to the ANFIS
framework are output of the EKS and the pre-processed abdominal ECG signal. The
fetal ECG signal is computed by subtracting output of the ANFIS from the
pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of
2013 physionet/computing in cardiology challenge database (PCDB) are used for
validation of the proposed methodology.
This thesis also describes a Field Programmable Gate Array (FPGA)
implementation of a heart rate calculation system using Electrocardiogram (ECG)
signal. The proposed FPGA based heart calculation system is FPGA implementation
of proposed R peak detection technique based on Shannon energy envelope with a little
modification. After heart rate calculation, tachycardia, bradycardia or normal heart
rate can easily be detected. Heart rate is calculated by detecting the R peaks from the
ECG signal. To provide a portable and the continuous heart rate monitoring system
for patients needs a dedicated hardware. FPGA provides easy testability, allows faster
implementation and verification option for implementing a new design. We have
proposed a five-stage based methodology by using basic VHDL blocks like addition,
multiplication and data conversion (real to the fixed point and vice-versa) etc for our
proposed design. The proposed FPGA based heart rate calculation (R-peak detection)
method shows better performance compared to other well-known methods for detection
of R peaks (heart rate calculation) and successfully implemented in FPGA.
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Hsieh, Tien-Chien, and 謝天健. "ECG/EEG Multi-Signal System Implemented on Mobile Devices." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/26509048476426153662.
Full textAbstract:
碩士健行科技大學
資訊工程所
101
Heart disease was one of the major causes of death in Taiwan for a long time. This situation shows that the heart disease should not be underestimated. It’s quite important to perform periodic health examinations to away from heart disease. But, it was usually inconvenient to perform periodic health examinations for the patients and the elders. Therefore, in this thesis, we proposed an ECG/EEG multi-signal system using mobile devices, we realized ECG/EEG signals capturing, long-time physiological monitoring, and then calculating the heart rate(R-R wave) and brainwave frequency(α,β,θ,δ wave). The system will auto determine the arrhythmia and brainwave status. Then, by use of mobile devices, we got the health conditions for a patient through 3G or Wi-Fi system. And then, the Electrocardiography(ECG) and Electroencephalography(EEG) signals were stored. Finally, we carry out a telemedicine system to prevent heart disease.
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Chen, Po-Chiang, and 陳柏強. "Distributed Compression of ECG Signal." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/50879412577389787733.
Full textAbstract:
碩士國立交通大學
電信工程研究所
99
In recent years, mobile telemedicine has become one of the emerging research topics. Electrocardiogram (ECG) is a pictorial representation of the electrical activity of heart beats and is useful diagnose cardiac disease. The purpose of this work is to develop distributed source coding (DSC) techniques for ECG signal compression under an ideal transmission environment. We focus on the DSC of two correlated ECG sources, with their correlation modeled by a virtual binary symmetric channel (BSC). Based on the concept of code binning, the DSC encoder compresses one source sequence into a syndrome sequence. The DSC decoder uses the other source sequence as side information together with the received syndrome sequence to identify the original source sequence. Also proposed is a modified BCJR algorithm which integrates the symbol level a priori information into the soft-output decoding algorithm.
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Kumar, Shailesh. "Denoising of Electriocardiogram (ECG) Signal." Thesis, 2017. http://ethesis.nitrkl.ac.in/8900/1/2017_MT_SKumar.pdf.
Full textAbstract:
Plenty of information related to physiology of the heart can be collected from the electrocardiogram (ECG) signal. In reality, the ECG signal is interfered by the various noise sources. To extract the correct information related to physiology of the heart, the cancellation of noise present in the ECG signal is needed. In this thesis, the investigation on effectiveness of the empirical mode decomposition (EMD) with non - local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework to preserve the edges of the ECG signal and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology based on EMD with NLM framework has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser percent root mean square difference (PRD), mean square error (MSE), and better SNR improvement compared to other well-known methods.
In this thesis comparison of the different technique for removal of muscle artifacts and baseline wander noise. Different methodology for removal of muscle artifacts are conventional filtering, wavelet denoising, and non-local mean (NLM) technique, in these wavelet denoising gives better SNR improvement and lesser MSE and PRD. Similarly for baseline wander removal, the performance of different techniques like two-stage median filter, single-stage median filter, two-stage moving average filter, single-stage moving average filter, low-pass filter, and band-pass filter have been evaluated using added baseline wander noise to synthetic ECG signal at different sampling frequency, among these two-stage median filter gives better SNR improvement and lesser MSE and PRD.
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Jain, Sanjeev Kumar. "Energy efficient ecg signal processor for wearable ecg diagnostic system." Thesis, 2016. http://localhost:8080/xmlui/handle/12345678/7213.
Full text
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"Wireless electrode for electrocardiogram (ECG) signal." 1999. http://library.cuhk.edu.hk/record=b5890078.
Full textAbstract:
by Leung Sze-wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (leaves 79-84).
Abstracts in English and Chinese.
ACKNOWLEDGEMENT --- p.II
ABSTRACT --- p.III
摘要 --- p.V
CONTENTS --- p.VI
Chapter CHAPTER 1 --- INTRODUCTION --- p.1
Chapter 1.1 --- Objectives --- p.1
Chapter 1.2 --- Prevalence of Heart Diseases --- p.1
Chapter 1.3 --- Importance of ECG Monitoring --- p.2
Chapter 1.4 --- Wireless Electrode --- p.2
Chapter 1.5 --- Analogue-to-Digital Converters --- p.3
Chapter 1.6 --- Organization of Thesis --- p.4
Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.5
Chapter 2.1 --- Telemetry --- p.5
Chapter 2.1.1 --- "Definitions of ""Telemetry “" --- p.5
Chapter 2.1.2 --- Advantages of Telemetry --- p.6
Chapter 2.1.3 --- History of Telemetry --- p.7
Chapter 2.1.4 --- Special Considerations on Telemetry System --- p.10
Chapter 2.2 --- Sigma-Delta Converter --- p.12
Chapter 2.2.1 --- Conventional Digitizing Circuitry --- p.12
Chapter 2.2.2 --- "Single, Dual-Slope A/D Converters" --- p.13
Single-Slope A/D Converter --- p.13
Dual-Slope Converter --- p.75
Chapter 2.2.3 --- Successive Approximation (SAR) --- p.17
Chapter 2.2.4 --- Flash Converters --- p.18
Chapter 2.2.5 --- Sigma-Delta Converter --- p.18
Chapter 2.3 --- Conclusion --- p.20
Chapter CHAPTER 3 --- WIRELESS ELECTRODE --- p.21
Chapter 3.1 --- """Single Electrode"" Measurement" --- p.21
Chapter 3.2 --- VSE (Virtual Single Electrode) --- p.21
Concentric Electrode --- p.21
Chapter 3.3 --- WE (Wireless Electrode) --- p.24
Chapter 3.4 --- Discussion --- p.29
Chapter CHAPTER 4 --- SIGMA-DELTA CONVERTER FOR ECG SIGNALS --- p.30
Chapter 4.1 --- Motivations --- p.30
Chapter 4.2 --- Baseband Application --- p.31
Chapter 4.2.1 --- Simulation Results --- p.31
Chapter 4.2.2 --- Experimental Results --- p.48
Chapter 4.3 --- Wireless Application --- p.58
Chapter 4.3.1 --- General Description --- p.58
Chapter 4.3.2 --- Simulation Results --- p.59
Chapter 4.3.3 --- Scenario 1 (Analogue Decoding) --- p.70
Chapter 4.3.4 --- Scenario II (Digital Decoding) --- p.73
Chapter 4.4 --- Discussion and Conclusion --- p.76
Chapter CHAPTER 5 --- CONCLUSION AND FUTURE WORK --- p.77
Chapter 5.1 --- General Conclus ion --- p.77
Chapter 5.2 --- Future Work --- p.78
BIBLIOGRAPHY --- p.79
LIST OF ABBREVIATIONS --- p.85
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Wu, Feng-Jen, and 吳鳳甄. "Pattern Recognition for ECG Signal Analysis." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/72521099851716263871.
Full textAbstract:
碩士國立清華大學
資訊系統與應用研究所
97
Heart disease is the third of the top ten causes of deaths in Taiwan these years. The heart disease cause of the cardiovascular disease is unexpected hence developing a real-time heart disease detector system is important. In this thesis we implement an ECG signal classification system with these stages: signal pre-processing, wavelet transform, and signal classification. In the first stage, median filter is used to reduce the noise and baseline wander on the ECG signal. Then we detect the QRS complex and extract the features of QRS complex by wavelet transform. Finally, we use nearest neighbor method to classify each pattern of QRS complex. A QRS complex is classified into one of the seven types: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), premature ventricular contraction (V), atrial premature beat (A), ventricular escape wave (E), and ventricular flatter wave (I). The QRS complex classification is tested on two databases: MIT-BIH Arrhythmia Database and the European ST-T Database. The experimental results can be divided into two parts: QRS complex detection and ECG signal classification. In QRS complex detection, the accurate detection rates are above 99% in these two databases. The recognition rates are about 97% and 99% in the MIT-BIH Arrhythmia Database and the European ST-T Database, respectively.
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Lin, Wen Hsiung, and 林文雄. "ECG signal detection via PDA phone." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/59976251132860306485.
Full textAbstract:
碩士長庚大學
電機工程學研究所
96
The portable electrocardiogram (ECG) recorders recently used for medical measurements. The recorded data can be either stored in memory unit for subsequent analysis in computer or wired or wireless transmitted to server. Our system is developed based on PDA phone due to computation capability, built-in Bluetooth technology, and post-hoc data transmission in PDA phone. The correct ECG analysis including R wave detection based on Pan-Tompkins algorithm and Q and S characteristic points detection, provides RR、QR、and RS interval analysis .Our system can be incorporated with 3G telephone system and geometrical localization system for telecare of patients with cardiovascular disease and for self-health management
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Huang, Chen-Wei, and 黃振維. "Signal Processing Techniques for ECG analysis." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/91598407019846520333.
Full textAbstract:
博士國立臺灣大學
電信工程學研究所
103
The electrocardiogram (ECG) signals provide important information about human heart status. The information of the human heart, such as the normal or irregular heartbeat rhythm, the heartbeat rate, and the working behaviors of heart, can be used to interpret healthy or unhealthy states of heart. An automatic ECG waveform analysis algorithm with high accuracy and efficiency is helpful for cardiac disease diagnosis and health monitoring. A typical heartbeat consists of the dominant points of P, Q, R, S, and T peaks. The most important one is the R-wave peak. When the position of the R-wave peak is found, P, Q, S, and T peaks can be determined according to the relative positions to the R-wave peak. After detecting P, Q, R, S, and T peaks, their locations, heights, widths, and distances are extracted as the basic features for heartbeat classification. The accuracy of cardiac disease problem analysis, such as premature ventricular contraction (VPC), atrial premature contraction (APC), and atrial fibrillation (AF) analysis, significantly depends on whether the features of an ECG signal can be extracted accurately. In the dissertation, we propose a time-domain-based algorithm, which is very effective and efficient, to analyze an ECG signal for heart disease diagnosis and health monitoring. Based on the signal processing techniques of the gradient varying weighting function for baseline subtraction of an ECG signal, the Haar-like matched filter, noise-like peaks removal by the variation ratio test, adaptive thresholds for R-wave peak sifting, and the Mexican-hat matched filter for detection P, Q, S, and T peaks, the intra-heartbeat and inter-heartbeat features can be extracted precisely. Moreover, a rule based weighted classifier with product-form score functions, a ratio variation hypothesis test method, and a two-class cluster splitting method by the Gini index are also applied for VPC heartbeat, APC heartbeat, and AF episode classification. The proposed real-time detection algorithm is tested in the MIT-BIH arrhythmia database, the atrial fibrillation database, the QT database, and the AHA database, which consist of two-lead ECG signals. Simulations show that the proposed algorithm achieves higher sensitivity value (SE), positive prediction rate (+P), detection error rate (DER), and specificity value (SP) than those of other existing algorithms. With the proposed signal processing techniques for ECG signal analysis, the PVC heartbeats, APC heartbeats, and AF episodes can be determined in an accurate way.
31
Kao, Ruei-Da, and 高睿達. "Possible Applications of ECG Signal Harmonics." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/50576856839472264983.
Full textAbstract:
碩士國立中山大學
機械與機電工程學系研究所
100
Via the delivery of blood, heart transfers oxygen and nutrients to various organs and is thus a highly influential for circulatory system. To adapt to the variation of physiological conditions, the intensity and frequency of heart beats change with time. Careful observation finds that the time intervals between heartbeats are often different even if the body is at rest. Such heart rate variability (HRV) has been used to estimate the activity of the autonomic nervous system which can be divided into sympathetic and parasympathetic subsystems both of which can significantly affect the physiology of the human body. As a result, HRV has been used as a physiological indicator to assist doctors in making diagnostic decisions. Many studies have used HRV to analyze the ECG signal via studying the QRS complex waveform to determine the time intervals between R-peaks and analyze the R-R intervals from time and frequency domains. Different from the conventional R-R Interval based approach, this work introduces new HRV feature variables by computing spectrogram of the ECG signal waveform. In particular, based on the harmonics of the spectrum, we introduce the concepts of modes. By find the relative amount of energy associated with each mode and degree-of-energy-concentration associated with each mode, this work introduces two sets of new HRV features. In addition, we also investigate how these variables change with time and the correlations between these features. To demonstrate the potential of the proposed features, the differences of the values of the proposed features are compared for healthy individuals versus OSA patients, young versus old and male versus female. The experimental results show the differences between many of the tested features are statistically significant.
32
Li, Hui-Chun, and 李慧君. "ECG Signal Quality Evaluation and Screening." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/any6t6.
Full textAbstract:
碩士國立中央大學
資訊工程學系在職專班
107
Electrocardiogram is likely to find very extensive use in many application areas in recent years, however, there is no standard electrocardiographic criteria in screening after collecting the ECG data, which leads researchers have to proceed quality estimation of the ECG signal before interpreting the analysis. For improving the traditional method of signal quality estimation by the naked eye or the improved threshold, are used as statistical indicators to identify with decision tree analysis screening. A total of 200 retrieved data through Physionet database, and a total of 238 collected signals by ECG sensors and simulators are analyzed in this study. A comparison between this study and previous studies’ database in the accuracy was 95% and 93.24%, and 83%, respectively. Therefore, this study result is not influenced by the devices and individual differences and both can maintain good accuracy. Through the high level of signal accuracy quality assessment, the burden for the ECG researchers can be efficiently reduced.
33
Gupta, Raviranjan. "Fetal ECG Extraction Methods." Thesis, 2015. http://ethesis.nitrkl.ac.in/7716/1/2015_Fetal_ECG_Gupta.pdf.
Full textAbstract:
The electrocardiogram signal of fetus, i.e; FECG express very clear information which helps doctors in making appropriate and timed decision during labor. The profound interest of FECG analysis is in the field of biomedical applications and clinical diagnosis. FECG is extracted from composite abdominal signals using advanced methodologies, and plays a pivotal role in automated fetal monitoring systems. In this thesis we have used various strategies and existing algorithms for FECG detection and analysis to facilitate proficient and detailed understanding of FECG and its role in monitoring of fetus. A comparison has been drawn to show the accuracy and performance of methods used for FECG signal analysis. Additionally, this thesis also throws some light on the hardware implementation for heart rate monitoring of the fetus. This paper clearly opens up a section for analysts, doctors, and end clients to promoter a superb comprehension of FECG sign and its investigation systems for observing framework for fetal heart rate .
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Huang, Wei-Chih, and 黃韋智. "A Low Noise EEG /ECG Signal Readout Frond-End and An ECG Motion Artifact Analog Detector for Telemedicine Mobile Biomedical Signal Acquisition Systems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/05585055291834087893.
Full textAbstract:
碩士國立清華大學
電機工程學系
101
In modern clinical practice, monitoring of biomedical signals is a crucial and important part. The biomedical signals most commonly used in medical diagnoses include EEG, ECG and EMG, etc. Therefore, there is a growing demand for small-size portable biomedical signal acquisition systems to improve the patients’ quality of life. The low noise biomedical signal readout frond-end consists of a chopper current-balancing instrumentation amplifier (CCBIA), a small Gm-C low pass filter, and a programmable gain amplifier. The CCBIA utilizes the chopper stabilization to reduce the flicker noise and amplifiers the low amplitude biomedical signals. The common mode rejection ratio (CMRR) is also the important specification because there are Electromagnetic Interference (EMI) and Electrostatic Field Interference (EFI) when measuring biomedical signals. Behind the CCBIA, a low-pass filter is needed to reduce the out-of-band noise higher than the biomedical signal of interest, and the programmable gain amplifier is used to avoid the output signal to be saturated. The analog ECG motion artifact detector is proposed to save the power consumption of motion artifact reduction in DSP. The article is fabricated by TSMC 90nm CMOS process. The measurement results shows that the readout frond-end achieves 107dB CMRR, the gain and the bandwidth are tunable for EEG and ECG, integrated input-referred noises are only 1.06 μVrms and 1.64 μVrms within EEG and ECG signal bandwidth. The ECG motion artifact analog detector can detect the skin-electrode impedance variation and the ECG signal variation successfully. The total power consumption is 22.17 μW.
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CHANG, CHE-WEI, and 張哲瑋. "ECG Signal Transmission By Using WiFi Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/21467940655240879615.
Full textAbstract:
碩士中華大學
電機工程學系
105
With the development of digital age, more and more medical information can easily query, a variety of medical equipment innovation. In this paper, we use WiFi to transmit ECG messages, which will be divided into two parts. The first part will improve the ECG measurement end, use the instrumentation amplifier constructed by the operational amplifier, the high-pass filter, the non-inverting amplifier, the level shifter, low-pass filter and notch filter to form ECG measurement equipment, with low cost advantages. The second part in order to reduce the size and reduce the complexity of wiring added to the NodeMCU module, and the use of WiFi as a bridge for the transmission, the use of Client-Server way, ECG message transmission out. In this study, we use the circuit simulation and implementation to verify the results. This study established a slim and light system and hrough the assistance of National Chip Systems Design Center (CIC) to make the PCB. Keyword : ECG , WiFi , Client-Server
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Chang, Hao, and 張皓. "Analyzing the spectrum of ECG signal waveform." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/85723737657980371721.
Full textAbstract:
碩士國立中山大學
機械與機電工程學系研究所
101
Since heart beats are not entirely regular, its variation is referred to as heart rate variability (HRV). HRV have been extensively used to characterize the physiological state. As a result, HRV has also been used to assist doctors in making diagnostic decisions. After finding time intervals between neighboring R-peaks, HRV features are typically obtained by analyzing the R-R intervals (RRIs) from time domain and frequency domain. Different from the traditional RRI based methods, this work generates features from the ECG signal waveform which is potentially very informative. Based on harmonics of the spectrum, we introduce the concept of modes. By finding the percentage of energy and degree of diffusion of each mode, this work proposes two sets of new features. We also investigate how these features and their correlations change with the physiological state. To verify the effectiveness of the proposed features, the differences of the mean values of these features are compared for old versus young and healthy controls versus PLMS patients. Experiments show that some of the proposed features exhibit statistically significant differences between the compared groups. In addition, using degree of diffusion, we can roughly differentiate the young and old persons. These results demonstrate the potential of the proposed approach in characterizing physiological state.
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Ho, Kuei-Jung, and 何貴榮. "Simple ECG signal analyzer for homecare system." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/kh59s2.
Full textAbstract:
碩士健行科技大學
電子工程系碩士班
105
This study presents a simple electrocardiogram (ECG) signal analyzer for homecare system. It can transmit ECG signals of patient around his/her house through Bluetooth (or Wi-Fi) to computers in house. ECG signals are analyzed by the computer. If abnormal case of heartbeat is found, the emergency call is automatically dialed. Meanwhile, the determined heartbeat case of ECG signals will be forwarded to patient''s MD through internet. Therefore, the patient can do whatever he/she wants around his/her house with our proposed simple cardiac arrhythmias signal analyzer. The proposed consists of five major processing stages: (i)preprocessing stage for enlarging ECG signals'' amplitude and eliminating noises; (ii) ECG signal ransmitter/receiver stage, ECG signals are transmitted through Bluetooth (or Wi-Fi) to the signal receiver in patient''s house; (iii) QRS extraction stage for detecting QRS waveform using the Difference Operation Method (DOM) method; (iv) qualitative features stage for qualitative feature selection on ECG signals; and (v) classification stage for determining patient''s heartbeat cases using the Principal Component Analysis (PCA) method. In the experiment, the total classification accuracy (TCA) obtained are 99.28%, 95.19%, 96.61%, and 97.03% for Tape number 103, 111, 118, and 123, respectively.
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WU, YEN-CHING, and 吳燕青. "ECG and PPG signal measurement device miniaturization." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/g675q5.
Full textAbstract:
碩士明志科技大學
電子工程系碩士班
106
As the Taiwanese population is rapidly aging, people become increasingly conscious about their personal health and home health management. Thus, the demand for home health measurement devices is increasing day by day. It is common to see physical measurement devices such as blood pressure gauge or electrocardiograph that are not convenient for portable use, which make us unable to pay more attention to assessing our physical and health condition freely at any time. The study aimed to explore the idea of cutting the electrical circuit and reducing the size of the health measurement device thus making it into a portable one, and equipping it with other features such as non-invasiveness and security. Besides, the author expects that the miniature physical measurement device could provide a simple reference for the user on his/her physical health condition. Keywords: Health Management, Physical Measurement, Miniature Devices
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Adithya, Godishala. "Denoising of ECG Signal Using TMS320C6713 Processor." Thesis, 2015. http://ethesis.nitrkl.ac.in/7447/1/2015_Denoising_Adithya.pdf.
Full textAbstract:
The point of this thesis is to DE noising of ECG signal using TMS320C6713 DSK processor. Here the use of adaptive filtering technique gives us the idea of effective method for filtering of a signal. The ECG signal is generated in Mat lab software and is used as the optimum source samples i.e., it is used as the desired signal which is to be compared with the input signal. Here we use LMS algorithm as an adaptive algorithm for processing and analyzing of the ECG signal. Adaptive filters are best utilized as a part of situations where signal settings or framework limitations are gradually varying and filter is confirmed to adjust for this change. There are many adaptive algorithms of which LMS algorithm is one of them and is more accurate and precise. The C6713 is modern DSK processor which has both floating and fixed point processors. The earlier versions only had the fixed point processing. The random signal is removed from the ECG signal with the help of LMS filter code that is loaded into CCS (Code Compressor studio) and the output from the processor is verified.
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Naru, Satish Kumar Reddy. "ECG Signal Reconstruction Using Interacting Multiple Model." Thesis, 2016. http://ethesis.nitrkl.ac.in/9344/1/2016_MT_SKRNaru.pdf.
Full textAbstract:
The Electrocardiography (ECG) is the method of reading the electrical movement of the heart over time by keeping electrodes on a body of a person. ECG signals are frequently studied to diagnose possible diseases in human body. It has become common for any medical evaluation and has been used for a long time. In recent days, computer-assisted ECG analysis is playing an important role in helping doctors in the study and treatment of heart abnormalities. an ECG can be used to measure the rate and rhythm of heartbeats, the size and position of the heart chambers, the presence of any damage to the heart’s muscle cells or conduction system, the effects of cardiac drugs, and the function of implanted pacemakers. ECG is used to check the health of the patient’s heart when other diseases or conditions are present. These include high blood pressure, high cholesterol, cigarette smoking, diabetes, and a family history of early heart disease. This makes the ECG signal the most enduring tool for the cardiologist. Many ECG signal modeling techniques have been proposed for compression and classification.
Polynomial Modeling of an ECG waveform is one of the oldest methods. But the main drawback of polynomial models is that we have to use the same type of polynomial for each point. However, fiducial points vary, depending on the person and the person’s cardiac health. That problem can be overcome by using different polynomial models to model the same ECG waveform which can be done by using the interacting multiple models (IMM) framework. The multi-mode property of this framework allows us to switch different modes of operation using first-order Markovian transition probabilities. Dynamical systems can be modeled with few possible modes of operation instead of single by using the IMM algorithm. The IMM algorithms use Kalman filters which are run in parallel. These individual filters are initialized by using results obtained from previous steps. Finally, the overall estimate can be given by mixing the estimates of individual filters. The IMM algorithm can be described using the interaction, filtering, and combination stages. This IMM algorithm provides us with the polynomial coefficients for each and every point in the ECG waveform. Similarly, we can extend this framework by replacing Kalman filter with an extended Kalman filter
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Kumar, Rajesh. "Development of Three Lead ECG Machine." Thesis, 2015. http://ethesis.nitrkl.ac.in/7714/1/2015_BT_Development_Rajesh_Kumar.pdf.
Full textAbstract:
An ECG device is used to monitor and analyze the heart activity. In this three lead ECG machine project, we have designed a hardware module for observing ECG signal. The main advantage of constructing a three lead ECG machine is taking measurement during transportation of the patient. The three lead ECG machine requires only patients’ limbs to take readings not whole chest area like in 12-lead ECG machine. Although in three lead ECG machine we can observe only two sides of human heart i.e. lateral side and inferior side of the heart. In an ECG machine construction various hardware component needed such as instrumentation amplifier, 741 OPAMP, electrodes, analog to digital conversion (ADC) module, filtering module and display module etc. The task of collecting various heart signals through the limbs of the patients’ is done using electrodes. It could be clamp electrode, adhesive electrode, patch electrode etc. The signals Collected through electrodes are fed to the instrumentation amplifier which basically is a differential amplifier, gets the ECG signals of millivolts from body and amplifies them with internal gain then passes them to filtering circuit for noise removal and setting the upper and lower frequency limitations on the ECG frequency. The signal obtained after the filtering is then again subjected to a gain amplifier for amplifying the final output ECG signals. The other artifact which influence the ECG signals like frequency distortion, naked wire, power line artifact, environmental problems are taken care for obtaining a good and useful ecg signal using which medical officer can do the analysis of the patients’ heart
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Bhuyan, Sandeepa, and Priyesh Patel. "Diagnosis of ECG arrhythmias in wireless telecardiology." Thesis, 2013. http://ethesis.nitrkl.ac.in/4870/1/109EI0170.pdf.
Full textAbstract:
Since many years ago, studies in the medical remote monitoring at home has taken a great consideration and care in wireless communication technology. The set of these studies is linked to needy people as aged ones, physically disabled in short time, in order to the adaptation with their environment domestically and build up their in capabilities. In this point of view, it is important to make a diagnostic in a real time and managed really the given data of patients between medical personnel with the permanent safety insurance of critical edge patients. Furthermore, the need to make a speed diagnostic of patients and to distinguish their health state with efficacy permits the gain of time in their taking off. Our attention has been aimed on the option of a relevant work. It concerns an function on a mobile terminal (MIDlet) for monitoring patient in a non-hospital environment. This article evokes a complete structural design of an economic wireless communication system with the implementation of an efficient algorithm, adapted to the mobile terminal, allowing to the doctor to have the results of analysis of ECG information wirelessly.
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Srivastava, Shubhranshu. "Denoising and Artifacts Removal in ECG Signals." Thesis, 2015. http://ethesis.nitrkl.ac.in/7444/1/2015_Denoising_Shrivastava.pdf.
Full textAbstract:
ECG signal is a non-stationary biological signal and plays a pivotal role in the diagnosis of cardiac-related abnormalities. Reduction of noise in electrocardiography signals is a crucial and important problem because the artifacts corrupting the signal possesses similar frequency characteristics as that of the signal itself. Conventional techniques viz. filtering were proved to be uncap able of eliminating these interferences. Therefore the electrocardiography signals require a novel and efficient denoising strategy with a view to facilitate satisfactory noise-removal performance. A new yet adaptive and data-driven method for denoising of ECG signals using EMD and DFA algorithms has been investigated...The proposed algorithm has been tested with ECG signals (MIT-BIH Database) with added noise such as baseline wander and muscle contraction noise. Parameter are calculated to determine the effectiveness of the algorithm on a variety of signal types. The obtained results show that the proposed denoising algorithm is easy to implement and suitable to be applied with electrocardiography signals.
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Vasimalla, Mounika. "ECG Arrhythmia Classification Using Convolutional Neural Networks." Thesis, 2018. http://ethesis.nitrkl.ac.in/9992/1/2018_MT_216ec6258_MVasimalla_ECG.pdf.
Full textAbstract:
Cardiac arrhythmias occur in a short duration of time which can’t be distinguishable by a human eye. Cardiac arrhythmia detection is a tedious task since slight changes in ECG signal may lead to life-threatening diseases. Diagnosis and medication at an early stage could help to the decrease the high mortality rate among the heart patients. This paper presents an accurate technique for the classification of five types of ECG arrhythmia namely Premature ventricular contraction(V), Normal (N), Left bundle branch block (L), Right bundle branch block (R), Paced (P). This technique incorporates convolutional neural networks (CNN) that combine both feature extraction, classification into a single body which restricts the use of complex feature extraction techniques like DTCWT (Dual tree complex wavelet transform) and a separate classifier to classify these features into appropriate classes. The performance of the proposed technique is assessed by using MIT-BIH arrhythmia database. Average classification accuracy of 95.43% is obtained which is superior to many other algorithms proposed in the literature.
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Liao, Shu-Wei, and 廖書偉. "Application of Biomedical / Dynamic Signal Processing in ECG." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/25113683709821417924.
Full textAbstract:
碩士國立中央大學
機械工程研究所
92
Abstract The main purpose of this study is to design an ECG measuring system for investigating the electrical activity of heart. At the measuring system, two frontal plane limb leads, lead II and III, are measured. The other four frontal plane leads can then be calculated by using the Einthoven triangle which is lead I, aVR, aVL, and aVF, respectively. The acquired analogue ECG signals are still not acceptable, further digital filter is needed to obtain improved illustration for clinic diagnosis. In the study, we provide four different types of filter to remove noise and compare their performance. To detect ECG peaks, we compute some information of ECG signals for diagnosis. In general, the heart rate computation is to employ R-R interval. But another method detecting heart rate in the spectrum is proposed in the study. One of the ECG signal processing targets is to smooth the data to reduce high frequency noise and to improve SNR in signals. The conventional technique can cause R-wave peaks reduction, such as using Agilent component monitoring system V24. Hence, the Gabor filtering technique is firstly proposed and implemented to cope with this drawback without missing any tiny information. Finally, we apply the spectrum and Gabor filters to obtain smoothed ECG signals for clinic or diagnostic purpose. Some information of an ECG such as heart rate and PR wave interval is computed by peak detection through proposed spectral analysis. They conclude the ECG signal processing in the study.
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Lin, Wei-Chu, and 林威助. "Biosignal Acquisition System Design and ECG Signal Preprocessing." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/55503001115639701339.
Full textAbstract:
碩士國立交通大學
電機與控制工程系
89
This thesis includes two parts. In Part I, due to the hardware limitation in our laboratory, the research work has been focused on the EEG (electroencephalograph) signal analysis. In order to explore more physiological phenomena during meditation and understand the correlation among different signals, we need to design and implement a data acquisition system capable of recording a number of physiological signals. This system includes three biological-signal amplifiers and one A-to-D interface card. The three amplifiers are used to measure the ECG (electrocardiograph), skin conductance, and blood oxygen saturation, respectively. This system and the NeuroScan EEG recording system can be triggered simultaneously by a switch to synchronize both systems. All of the hardware control is set up on the bench of Labview software for convenience of programming development and maintenance. In addition, the development of man-machine interface is straightforward using the Labview software. Part II of the thesis is devoted to the preprocessing of ECG signal. First, we aim to design an algorithm for R wave detection. Next, the ECG signal is segmented, based on the R-wave detection, to form a 2D matrix. The results can be further analyzed, in both time and frequency domains, by applying sophisticated DSP (digital signal processing) methods. This study will promote the understanding of more physiological states in meditation.
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曾鴻竣. "Robust Distributed Source Coding for ECG Signal Compression." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/93385706750904987551.
Full textAbstract:
碩士國立交通大學
電信工程研究所
100
In recent years, with the upgrading of the standard of living, as well as medical technology advances, wireless telemedicine and health-care has been one of the emerging research topics due to an aging society which is an inevitable trend. The electrocardiogram（ECG）records the changes of the heart beat-related potential, commonly used in the diagnosis and treatment of cardiovascular diseases. This thesis proposes a method for ECG compression based on distributed source coding. The compression ratio and the reconstruction quality depend on the source-related model, as well as syndrome former, therefore, we use gain-shaped vector quantization with multiple choice and binary switch algorithm to construct an optimized source-related model. Furthermore, by using trellis diagram formed from extended parity check matrix and BCJR decoding algorithm, we can improve the robustness of distributed source coding against binary symmetric channel errors.
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Chen, Hung-Chin, and 陳弘晉. "Automatic ECG Signal Classification Using Hidden Markov Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/65907981354140795550.
Full textAbstract:
碩士國立高雄大學
資訊工程學系碩士班
101
The heart is one of the most important organs in the human body. When the heart cannot pump regularly, it is called cardiac arrhythmia. During an arrhythmia, people may feel dizzy and pain in the chest. Furthermore, serious cardiac arrhythmias can result in death. Doctors and experts diagnose arrhythmias by using an electrocardiogram (ECG) because there are numerous useful information in ECG signal. Visual interpretation of ECG is a time-consuming process, therefore, we adopt a probabilistic approach based on Hidden Markov Model (HMM) for classifying ECG signal automatically. The ECG signals being classified including normal heartbeats, left bundle branch block heartbeats, right bundle branch block heartbeats, atrial premature complex heartbeats and premature ventricular contraction heartbeats. In order to perform well in classifying, we propose three different types of classification frameworks in our research. Besides, we use different amount of ECG features to train HMM models and classify ECG signals when processing different kinds of ECG signals. The results of the experiments show that the proposed method for automatic ECG signal classification is efficient and reliable.
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Chang, Wan-Teseng, and 張萬增. "QRS Complexes Detection for ECG Signal usingPTF Method." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/42577468500805538867.
Full textAbstract:
碩士健行科技大學
電子工程系碩士班
103
This study presents a novel Pan-Tompkins-Filter (PTF) algorithm for detecting QRS complexes of ECG signals. The PTF algorithm can effectively find the correct point of QRS complexes. The proposed algorithm consists of three major procedures: (1) the preprocessing procedure for noise reduction of ECG signals and taking PTF signals; (2) the procedure for finding R point by searching maximum and minimum extreme values, merging extreme values, setting threshold value, and reducing redundant extreme values; (3) the procedure for finding Q and S points by searching the minimum amplitude of search intervals of Q and S. The proposed PTF algorithm achieves about 99.25% classification accuracy on MIT-BIH and takes only 30 seconds for 10-minute long ECG signals. The PTF algorithm is a simple and effective algorithm for detecting QRS complexes of ECG signals.
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Wu, Hung-Tsai, and 吳鴻材. "A Mobile ECG Signal Analysis and Monitoring System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/60923745340762927351.
Full textAbstract:
博士國立交通大學
電信工程研究所
104
Wireless patient monitoring has been of recent interest to academic and industrial circles with the goal of ubiquitous healthcare services. The purpose of this dissertation is to investigate three important aspects of a mobile ECG signal monitoring system: data compression, robust data transmission, and biometric identification. In this study, we present a novel means of exploiting the distributed source coding (DSC) in low-complexity encoding of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit the convolutional code for practical DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source’s a priori information. A complete setup system for online ambulatory ECG signal monitoring via mobile cellular networks is also presented. As a further step toward increased robustness against transmission errors, we also investigate the noisy-channel DSC problem for ECG data compression in conjunction with variable-length codes (VLCs) and channel codes. Using the concept of extrinsic information transfer (EXIT) from Turbo codes, we present a symbol-level iterative source-channel decoding (ISCD) algorithm for reliable transmission of variable-length encoded ECG data. Firstly, an improved source a posteriori probability (APP) decoding approach is proposed for packetized variable-length codes. Also proposed is a recursive implementation based on a three-dimensional (3-D) joint trellis for symbol decoding of binary convolutional codes. APP channel decoding on this joint trellis is realized by modification of the BCJR algorithm and adaptation to the non-stationary VLC trellis. The proposed symbol-level ISCD algorithm allows the receiver to exploit the source residual redundancy as well as the channel code redundancy to the fullest extent as it avoids the conventional symbol-to-bit probability conversion problem between the two constituent decoders. Another important issue to address is the demand for improved security and privacy in wireless telecardiology applications. To this end, we propose a novel ECG biometric system which performs person identification using content-based image retrieval (CBIR) techniques. To proceed with this, 1-D ECG signals are converted to 2-D images and afterwards part of the JPEG2000 encoding process is applied. Features relating to ECG morphology are then computed directly from the DWT coefficients and applied for indexing person identity by texture content in an enrollment database.