Relevant bibliographies by topics / PCG signals

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'PCG signals'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "PCG signals"

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Damani, Devanshi N., Divaakar Siva Baala Sundaram, Shivam Damani, Anoushka Kapoor, Adelaide M. Arruda Olson, and Shivaram P. Arunachalam. "INVESTIGATION OF SYNCHRONIZED ACQUISITION OF ELECTROCARDIOGRAM AND PHONOCARDIOGRAM SIGNALS TOWARDS ELECTROMECHANICAL PROFILING OF THE HEART." Biomedical Sciences Instrumentation 57, no. 2 (April 1, 2021): 305–12. http://dx.doi.org/10.34107/yhpn9422.04305.

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Cardiac diseases are the leading cause of death in the world. Electrocardiogram (ECG and Phonocardiogram (PCG signals play a significant role in the diagnosis of various cardiac diseases. Simultaneous acquisition of ECG and PCG signals can open new avenues of signal processing approaches for electromechanical profiling of the heart. However, there are no standard approaches to ensure high fidelity synchronous data acquisition to enable the development of such novel technologies. In this work, the authors report results on various data capture positions that could lead to standardization of simultaneous ECG and PCG data collection. Presence of lung sounds, variations in posture, depth and frequency of breathing can lead to differences in the ECG-PCG signals recorded. This necessitates a standard approach to record and interpret the data collected. The authors recorded ECG-PCG simultaneously in six healthy subjects using a digital stethoscope to understand the differences in signal quality in various recording positions (prone, supine, bending, semi recumbent, standing, left lateral and sitting with normal and deep breathing conditions. The collected digitized signals are processed offline for signal quality using custom MATLAB software for SNR. The results indicate minimal differences in signal quality across different recording positions. Validation of this technique with larger dataset is required. Future work will investigate changes in characteristic ECG and PCG features due to position and breathing patterns.
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DEBBAL, S. M. "Heart Cardiac Sounds analysis using the Wigner distribution (WD) Method." Clinical Cardiology and Cardiovascular Interventions 04, no. 15 (September 20, 2021): 01–04. http://dx.doi.org/10.31579/2641-0419/216.

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This paper is concerned a “The Wigner distribution (WD)” analysis of the Heart cardiac (or phonocardiogram signals: PCG). The Wigner distribution (WD) and the corresponding WVD (Wigner Ville Distribution) have shown good performances in the analysis of non-stationary and quantitative measurements of the time-frequency PCG signal characteristics. It is shown that these transforms provides enough features of the PCG signals that will help clinics to obtain diagnosis.
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DEBBAL, S. M., and F. BEREKSI-REGUIG. "DISCRIMINATION OF PATHOLOGICAL CASES OF THE CARDIACS SOUNDS SIGNALS BY THE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 05, no. 04 (December 2005): 517–30. http://dx.doi.org/10.1142/s0219519405001679.

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In order to highlight the cardiac sounds or phonocardiogram (PCG) signals analysis according to their added murmur importance, we try to apply the wavelet transform in its multi resolution analysis version. We then look for reconstruction error between the original signal and the synthesized signal. In this case, the original PCG signal is decomposed over seven levels and the seventh detail of decomposition is considered as the synthesized signal. According to the results we obtain, the reconstruction error can be considered as an important parameter in the classification and discrimination of the pathological severity of the PCG signals.
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Pauline, S. Hannah, Samiappan Dhanalakshmi, R. Kumar, R. Narayanamoorthi, and Khin Wee Lai. "A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal." Journal of Healthcare Engineering 2022 (April 30, 2022): 1–24. http://dx.doi.org/10.1155/2022/3039624.

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Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.
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DEBBAL, S. M., and F. BEREKSI-REGUIG. "HEARTBEAT SOUND ANALYSIS WITH THE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 04, no. 02 (June 2004): 133–41. http://dx.doi.org/10.1142/s0219519404000916.

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This paper is concerned with a synthesis study of the continuous wavelet transform (CWT) in analyzing the phonocardiogram (PCG). It is shown that the CWT provides enough features of the PCG signals that will help physicians to obtain qualitative and quantitative measurements of the time and the time-frequency PCG signal characteristics, and consequently aid to diagnosis.
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DEBBAL, S. M., F. BEREKSI-REGUIG, and A. MEZIANE TANI. "THE FAST FOURIER TRANSFORM AND THE CONTINUOUS WAVELET TRANSFORM ANALYSIS OF THE PHONOCARDIOGRAM SIGNAL." Journal of Mechanics in Medicine and Biology 04, no. 03 (September 2004): 257–72. http://dx.doi.org/10.1142/s0219519404001028.

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This paper is concerned with a synthesis study of the fast Fourier transform (FFT) and the continuous wavelet transform (CWT) in analysing the phonocardiogram signal (PCG). It is shown that the continuous wavelet transform provides enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency PCG signal characteristics and consequently aid to diagnosis. Similary, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties.
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YANG, LIJUN, SHUANG LI, ZHI ZHANG, and XIAOHUI YANG. "CLASSIFICATION OF PHONOCARDIOGRAM SIGNALS BASED ON ENVELOPE OPTIMIZATION MODEL AND SUPPORT VECTOR MACHINE." Journal of Mechanics in Medicine and Biology 20, no. 01 (February 2020): 1950062. http://dx.doi.org/10.1142/s0219519419500623.

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The prevention and diagnosis of cardiovascular diseases have become one of the primary problems in the medical community since the mortality of this kind of diseases accounts for 31% of global deaths in 2016. Heart sound, which is an important physiological signal of human body, mainly comes from the pulsing of cardiac structures and blood turbulence. The analysis of heart sounds plays an irreplaceable role in early diagnosis of heart disease since they contain a large amount of pathological information about each part of human heart. Heart sounds can be detected and recorded by Phonocardiogram (PCG). As a noninvasive method to detect and diagnose heart disease, PCG signals have been paid more and more attention by researchers. In this paper, a novel envelope extraction model is proposed and used to estimate the cardiac cycle of each PCG signal. We present a strategy combining empirical mode decomposition (EMD) technique and the proposed envelope model to extract the time-domain features. After applying EMD process to each PCG signal, the second intrinsic mode function is chosen for further analysis. Based on the proposed envelope model, the cardiac cycles of PCG signals can be estimated and then the time-domain features can be extracted. Combining with the frequency-domain features and wavelet-domain features, the feature vectors are obtained. Finally, the support vector machine (SVM) classifier is used to detect the normal and abnormal PCG signals. Two public datasets are used to test our framework in this paper. And classification accuracies of more than [Formula: see text] on both datasets show the effectiveness of the proposed model.
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Chien, Ying-Ren, Kai-Chieh Hsu, and Hen-Wai Tsao. "Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications." Applied Sciences 10, no. 17 (August 24, 2020): 5842. http://dx.doi.org/10.3390/app10175842.

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Phonocardiography (PCG) signals that can be recorded using the electronic stethoscopes play an essential role in detecting the heart valve abnormalities and assisting in the diagnosis of heart disease. However, it consumes more bandwidth when transmitting these PCG signals to remote sites for telecare applications. This paper presents a deep convolutional autoencoder to compress the PCG signals. At the encoder side, seven convolutional layers were used to compress the PCG signals, which are collected on the patients in the rural areas, into the feature maps. At the decoder side, the doctors at the remote hospital use the other seven convolutional layers to decompress the feature maps and reconstruct the original PCG signals. To confirm the effectiveness of our method, we used an open accessed dataset on PHYSIONET. The achievable compress ratio (CR) is 32 when the percent root-mean-square difference (PRD) is less than 5%.
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Aziz, Sumair, Muhammad Umar Khan, Majed Alhaisoni, Tallha Akram, and Muhammad Altaf. "Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features." Sensors 20, no. 13 (July 6, 2020): 3790. http://dx.doi.org/10.3390/s20133790.

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Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.
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Berraih, Sid Ahmed, Yettou Nour Elhouda Baakek, and Sidi Mohammed El Amine Debbal. "Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals." Polish Journal of Medical Physics and Engineering 27, no. 1 (March 1, 2021): 73–85. http://dx.doi.org/10.2478/pjmpe-2021-0010.

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Abstract Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition. The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities. This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.
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Dissertations / Theses on the topic "PCG signals"

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Жемчужкіна, Т. В., and Т. В. Носова. "Construction of bispectra for PCG signals." Thesis, НТУ «ХПІ», 2021. https://openarchive.nure.ua/handle/document/17555.

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To construct bispectra for separate segments of PCG such as sounds and murmurs we applied segmentation method based on averaged Shannon envelope. We got bispectrums of sounds and murmurs for different signals: normal and pathological. It can be seen that bispectra are non-zero, so signals are non-Gaussian. There is phase coupling between each two frequencies for each bispectrum. And also, the patterns for bispectra for each person are different in all groups.
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Жемчужкіна, Т. В., and Т. В. Носова. "Сonstruction of phase portraits of PCG signals." Thesis, НТУ «ХПІ», 2021. https://openarchive.nure.ua/handle/document/17554.

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For PCG signals we constructed phase portraits (PP) in 2-dimensional state space. To demonstrate PP of heart sounds and murmurs we extracted main sounds from PCG signals. Different signals have different forms of PP and samples of main sounds can be separated from rest samples using PP. Showed applications of PP analysis. So, PP of PCG signals probably can be used for diagnostics of heart sounds and for segmentation of PCG signal without simultaneous ECG-recording.
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Daura, Ashiru Sani. "A wavelet-based method for the classification of PCG signals." Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244474.

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Giacotto, Luca. "Signal integrity at PCB level." Université Joseph Fourier (Grenoble), 2003. http://www.theses.fr/2003GRE10075.

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Bondurant, Philip D., and Andrew Driesman. "Smart PCM Encoder." International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/611601.

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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada
In this paper, a new concept in PCM telemetry encoding equipment is described. Existing "programmable" PCM encoders allow only simple changes in the functionality of the hardware, such as input gain, offset, and word formatting. More importantly, these encoders do not provide capability for "in-flight" processing of signals and in general have not taken advantage of existing hardware and software digital signal processing technology. In-flight processing of signals can provide a significant reduction in the required transmission bandwidth, allowing additional data that may not have otherwise been transmitted to be sent on the telemetry channel. A modular digital signal processor (DSP) based PCM encoder architecture is described that has a set of on-board processing algorithms configurable via a simple-to-use graphical user interface. Algorithms included are compression (lossy and lossless), Fourier transforms of various resolutions (typically followed by peak detection to provide a data rate reduction), extreme values (max, min, rms), time filtering, regression, trajectory prediction, and serial data stream processing. Custom algorithms can be developed and included as part of the suite of processing algorithms. The preprocessing algorithms exist as firmware on the DSPs and can accommodate as many different signals as the processing bandwidth of the DSP can handle. Typically one DSP can handle many input signals and different algorithms. The encoder is programmable via a standard RS-232 serial interface allowing the signal input configuration, telemetry frame layout, and on-board processing algorithms to be changed quickly.
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Noorzadeh, Saman. "Extraction de l'ECG du foetus et de ses caractéristiques grâce à la multi-modalité." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT135/document.

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La surveillance de la santé foetale permet aux cliniciens d’évaluer le bien-être du foetus,de faire une détection précoce des anomalies cardiaques foetales et de fournir les traitementsappropriés. Les développements technologies actuels visent à permettre la mesurede l’électrocardiogramme (ECG) foetal de façon non-invasive afin d’extraire non seulementle rythme cardiaque mais également la forme d’onde du signal. Cet objectif est rendudifficile par le faible rapport signal sur bruit des signaux mesurés sur l’abdomen maternel.Cette mesure est donc toujours un challenge auquel se confrontent beaucoup d’études quiproposent des solutions de traitement de signal basées sur la seule modalité ECG.Le but de cette thèse est d’utiliser la modélisation des processus Gaussiens pour améliorerl’extraction des signaux cardiaques foetaux, dans une base multi-modale. L’ECG est utiliséconjointement avec le signal Phonocardiogramme (PCG) qui peut apporter une informationcomplémentaire à l’ECG. Une méthode générale pour la modélisation des signauxquasi-périodiques est présentée avec l’application au débruitage de l’ECG et à l’extractionde l’ECG du foetus. Différents aspects de la multi-modalité (synchronisation, · · · ) proposéesont étudiées afin de détecter avec plus de robustesse les battements cardiaques foetaux.La méthode considère l’application sur les signaux ECG et PCG à travers deux aspects:l’aspect du traitement du signal et l’expérimental. La modélisation des processus Gaussien,avec le signal PCG pris comme la référence, est utilisée pour extraire des modèles flexibleset des estimations non linéaires de l’information. La méthode cherche également à faciliterla mise en oeuvre pratique en utilisant un codage 1-bit des signaux de référence.Le modèle proposé est validé sur des signaux synthétiques et également sur des donnéespréliminaires réelles qui ont été enregistrées afin d’amorcer la constitution d’une base dedonnées multi-modale synchronisée. Les premiers résultats montrent que la méthode permettraà terme aux cliniciens d’étudier les battements cardiaques ainsi que la morphologiede l’ECG. Ce dernier aspect était jusqu’à présent limité à l’analyse d’enregistrements ECGinvasifs prélevés pendant l’accouchement par le biais d’électrodes posées sur le scalp dufoetus
Fetal health must be carefully monitored during pregnancy to detect early fetal cardiac diseases, and provide appropriate treatment. Technological development allows a monitoring during pregnancy using the non-invasive fetal electrocardiogram (ECG). Noninvasive fetal ECG is a method not only to detect fetal heart rate, but also to analyze the morphology of fetal ECG, which is now limited to analysis of the invasive ECG during delivery. However, the noninvasive fetal ECG recorded from the mother's abdomen is contaminated with several noise sources among which the maternal ECG is the most prominent.In the present study, the problem of noninvasive fetal ECG extraction is tackled using multi-modality. Beside ECG signal, this approach benefits from the Phonocardiogram (PCG) signal as another signal modality, which can provide complementary information about the fetal ECG.A general method for quasi-periodic signal analysis and modeling is first described and its application to ECG denoising and fetal ECG extraction is explained. Considering the difficulties caused by the synchronization of the two modalities, the event detection in the quasi-periodic signals is also studied which can be specified to the detection of the R-peaks in the ECG signal.The method considers both clinical and signal processing aspects of the application on ECG and PCG signals. These signals are introduced and their characteristics are explained. Then, using PCG signal as the reference, the Gaussian process modeling is employed to provide the possibility of flexible models as nonlinear estimations. The method also tries to facilitate the practical implementation of the device by using the less possible number of channels and also by using only 1-bit reference signal.The method is tested on synthetic data and also on real data that is recorded to provide a synchronous multi-modal data set.Since a standard agreement for the acquisition of these modalities is not yet taken into much consideration, the factors which influence the signals in recording procedure are introduced and their difficulties and effects are investigated.The results show that the multi-modal approach is efficient in the detection of R-peaks and so in the extraction of fetal heart rate, and it also provides the results about the morphology of fetal ECG
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Beya, Ouadi. "Analyse et reconnaissance de signaux vibratoires : contribution au traitement et à l'analyse de signaux cardiaques pour la télémédecine." Thesis, Dijon, 2014. http://www.theses.fr/2014DIJOS015/document.

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Le coeur est un muscle. Son fonctionnement mécanique est celui d'une pompe chargée de distribuer et de récupérer le sang dans les poumons et dans le système cardiovasculaire. Son fonctionnement électrique est régulé par le son noeud sinusal, un stimulateur ou régulateur électrique chargé de déclencher les battements naturels du coeur qui rythment le fonctionnement du corps. Les médecins surveillent ce fonctionnement électromécanique du coeur en enregistrant un signal électrique appelé électrocardiogramme (ECG) ou un signal sonore : le phono-cardiogramme (PCG). L'analyse et le traitement de ces deux signaux sont fondamentaux pour établir un diagnostic et aider à déceler des anomalies et des pathologies cardiaques. L’objectif de cette thèse est de développer des techniques de traitement des signaux ECG et notamment PCG afin d’aider le médecin dans son analyse de ces signaux. L’idée de fond est de mettre en point des algorithmes relativement simples et peu coûteux en temps de calcul. Le premier intérêt serait de garantir leur implantation aisée dans un système mobile de surveillance cardiaque à l’usage du médecin, voire du patient. Le deuxième intérêt réside dans la possibilité d’une analyse automatique en temps réel des signaux avec le dispositif mobile, autorisant le choix de la transmission de ces signaux pour une levée de doute. De nombreux travaux ont mené à des avancées significatives dans l’analyse des signaux ECG et la reconnaissance automatiques des pathologies cardiaques. Des bases de données de signaux réels ou synthétiques annotées permettent également d’évaluer les performances de toute nouvelle méthode. Quant aux signaux PCG, ils sont nettement moins étudiés, difficiles à analyser et à interpréter. Même si les grandes familles de méthodes (Fourier, Wigner Ville et ondelettes) ont été testées, elles n’autorisent pas une reconnaissance automatique des signatures, d’en avoir une analyse et une compréhension assez fines.La Transformée en Ondelettes (TO) sur les signaux cardiaques a montré son efficacité pour filtrer et localiser les informations utiles mais elle fait intervenir une fonction externe de traitement (ondelette mère) dont le choix dépend de la connaissance au préalable du signal à traiter. Ce n'est pas toujours adapté aux signaux cardiaques. De plus, la Transformée en ondelettes induit généralement une imprécision dans la localisation due à la fonction externe et éventuellement au sous-échantillonnage des signatures. La nature non stationnaire de l'ECG et du PCG et leur sensibilité aux bruits rendent difficile la séparation d’une transition informative d'une transition due aux bruits de mesure. Le choix de l'outil de traitement doit permettre un débruitage et une analyse de ces signaux sans délocalisation des singularités ni altération de leurs caractéristiques. En réponse à nos objectifs et considérant ces différents problèmes, nous proposons de nous appuyer principalement sur la décomposition modale empirique (EMD) ou transformée de Hilbert Huang (THH) pour développer des solutions. L’EMD est une approche non linéaire capable de décomposer le signal étudié en fonctions modales intrinsèques (IMF), oscillations du type FM-AM, donnant ainsi une représentation temps/échelle du signal. Associée à la transformée de Hilbert (TH), la THH permet de déterminer les amplitudes instantanées (AI) et les fréquences instantanées (FI) de chaque mode, menant ainsi à une représentation temps/fréquence des signaux.Sans faire intervenir une fonction externe, on peut ainsi restaurer (réduction de bruit), analyser et reconstruire le signal sans délocalisation de ses singularités. Cette approche permet de localiser les pics R de l'ECG, déterminer le rythme cardiaque et étudier la variabilité fréquentielle cardiaque (VFC), localiser et analyser les composantes des bruits B1 et B2 du PCG
The heart is a muscle. Its mechanical operation is like a pump charged for distributing and retrieving the blood in the lungs and cardiovascular system. Its electrical operation is regulated by the sinus node, a pacemaker or electric regulator responsible for triggering the natural heart beats that punctuate the functioning of the body.Doctors monitor the electromechanical functioning of the heart by recording an electrical signal called an electrocardiogram (ECG) or an audible signal : the phonocardiogram (PCG). The analysis and processing of these two signals are essential for diagnosis, to help detect anomalies and cardiac pathologies.The objective of this thesis is to develop signal processing tools on ECG and PCG to assist cardiologist in his analysis of these signals. The basic idea is to develop algorithms of low complexity and having inexpensive computing time. The primary interest is to ensure their easy implementation in a mobile heart monitoring system for use by the doctor or the patient. The second advantage lies in the possibility of automatic real-time analysis of signals with the mobile device, allowing control of the transmission of these signals to a removal of doubt.Numerous studies have led to significant advances in the analysis of ECG signals and the automatic recognition of cardiac conditions. Databases of real or synthetic signals annotated also assess the performance of new methods. PCG signals are much less studied, difficult to analyze and to interpret. The main methods (Fourier, wavelet and Wigner Ville) were tested : they do not allow automatic recognition of signatures, and an accurate understanding of their contents.Wavelet Transform (WT) on cardiac signals showed its effectiveness to filter and locate useful information, but it involves an external processing function (mother wavelet) whose the choice depends on the prior knowledge on the signal to be processed. This is not always suitable for cardiac signals. Moreover, the wavelet transform generally induces inaccuracies in the location due to the external function and optionally due to the sub- sampling of the signatures.The non-stationary nature of the ECG and PCG and their sensitivity to noise makes it difficult to separate an informative transition of a transition due to measurement noise. The choice of treatment tool should allow denoising and analysis of these signals without alteration or the processing tool delocalization of the singularities.In response to our objectives and considering these problems, we propose to rely primarily on empirical mode decomposition (EMD) and Hilbert Huang Transform (HHT) to develop solutions. The EMD is a non linear approach decomposing the signal in intrinsic signal (IMF), oscillations of the type FM-AM, giving a time/scale signal representation. Associated with the Hilbert transform (TH), the THH determines the instantaneous amplitude (IA) and instantaneous frequency (IF) of each mode, leading to a time/frequency representation of the ECG and PCG.Without involving an external function, EMD approach can restore (noise reduction), analyze and reconstruct the signal without relocation of its singularities. This approach allows to locate R peaks of the ECG, heart rate and study the cardiac frequency variability (CFV), locate and analyze the sound components B1 and B2 of the PCG.Among the trials and developments that we made, we present in particular a new method (EDA : empirical denoising approach) inspired by the EMD approach for denoising cardiac signals. We also set out the implementation of two approaches for locating ECG signature (QRS complex, T and P waves). The first is based on the detection of local maxima : in using Modulus Maxima and Lipschitz exponent followed by a classifier. The second uses NFLS, wich an nonlinear approach for the detection and location of unique transitions in the discrete domain
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Law, E. L. "RF SPECTRAL CHARACTERISTICS OF RANDOM PCM/FM AND PSK SIGNALS." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612122.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada
The telemetry radio frequency (RF) spectrum is rapidly becoming more crowded. Therefore, telemetry system engineers and frequency managers must become more knowledgeable about the RF spectral characteristics of telemetry signals. This paper presents methods to calculate the expected RF spectrum of random non-return-to-zero (NRZ) pulse code modulation (PCM)/frequency modulation (FM) and phase shift key (PSK) signals. The discussion includes the effects of bit rate, peak deviation, premodulation filtering, and spectrum analyzer resolution bandwidth. The methods are easily implemented using a personal computer and a spreadsheet program with graphics capability. Calculated spectra agree well with measured spectra. Equations are presented for accurately estimating the peak deviation and unmodulated carrier power of a random NRZ PCM/FM signal from the measured RF spectrum. Adjacent channel interference is also calculated. Key words: radio frequency spectral occupancy, pulse code modulation, frequency modulation, phase shift keying, premodulation filtering, adjacent channel interference.
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Ahlström, Christer. "Nonlinear phonocardiographic Signal Processing." Doctoral thesis, Linköpings universitet, Fysiologisk mätteknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11302.

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The aim of this thesis work has been to develop signal analysis methods for a computerized cardiac auscultation system, the intelligent stethoscope. In particular, the work focuses on classification and interpretation of features derived from the phonocardiographic (PCG) signal by using advanced signal processing techniques. The PCG signal is traditionally analyzed and characterized by morphological properties in the time domain, by spectral properties in the frequency domain or by nonstationary properties in a joint time-frequency domain. The main contribution of this thesis has been to introduce nonlinear analysis techniques based on dynamical systems theory to extract more information from the PCG signal. Especially, Takens' delay embedding theorem has been used to reconstruct the underlying system's state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction. In this thesis, the PCG signal's structure in state space has been exploited in several applications. Change detection based on recurrence time statistics was used in combination with nonlinear prediction to remove obscuring heart sounds from lung sound recordings in healthy test subjects. Sample entropy and mutual information were used to assess the severity of aortic stenosis (AS) as well as mitral insufficiency (MI) in dogs. A large number of, partly nonlinear, features was extracted and used for distinguishing innocent murmurs from murmurs caused by AS or MI in patients with probable valve disease. Finally, novel work related to very accurate localization of the first heart sound by means of ECG-gated ensemble averaging was conducted. In general, the presented nonlinear processing techniques have shown considerably improved results in comparison with other PCG based techniques. In modern health care, auscultation has found its main role in primary or in home health care, when deciding if special care and more extensive examinations are required. Making a decision based on auscultation is however difficult, why a simple tool able to screen and assess murmurs would be both time- and cost-saving while relieving many patients from needless anxiety. In the emerging field of telemedicine and home care, an intelligent stethoscope with decision support abilities would be of great value.
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Hákonardóttir, Stefanía. "Prosthetic Control using Implanted Electrode Signals." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-147699.

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This report presents the design and manufacturing process of a bionic signal messagebroker (BSMB), intended to allow communication between implanted electrodes andprosthetic legs designed by Ossur. The BSMB processes and analyses the data intorelevant information to control the bionic device. The intention is to carry out eventdetection in the BSMB, where events in the muscle signal are matched to the events ofthe gait cycle (toe-o, stance, swing).The whole system is designed to detect muscle contraction via sensors implantedin residual muscles and transmit the signals wireless to a control unit that activatesassociated functions of a prosthetic leg. Two users, one transtibial and one transfemoral,underwent surgery in order to get electrodes implantable into their residual leg muscles.They are among the rst users in the world to get this kind of implanted sensors.A prototype of the BSMB was manufactured. The process took more time thanexpected, mainly due to the fact that it was decided to use a ball grid array (BGA)microprocessor in order to save space. That meant more complicated routing and higherstandards for the manufacturing of the board. The results of the event detection indicatethat the data from the implanted electrodes can be used in order to get sucient controlover prosthetic legs. These are positive ndings for users of prosthetic legs and shouldincrease their security and quality of life.It is important to keep in mind when the results of this report are evaluated that allthe testing carried out were only done on one user each.
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Books on the topic "PCG signals"

1

Shukla, Vikas. Signal integrity for PCB designers. Attleboro, MA: Reference Designer Inc., 2009.

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Introduction to PCM telemetering systems. 2nd ed. Boca Raton: CRC Press, 2002.

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Introduction to PCM telemetering systems. Boca Raton: CRC Press, 1993.

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Maes, Dominiek, Marina Sibila, and Maria Pieters, eds. Mycoplasmas in swine. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249941.0000.

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Abstract This book contains 14 chapters that discuss the genetics, epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma infections in pigs. Chapter 1 discusses the phylogenetics and classification of Mycoplasma species in pigs; Chapter 2 describes the genomic diversity and antigenic variation of Mycoplasma hyopneumoniae strains; Chapter 3 discusses the pathogenesis, virulence factor and pathogenicity of Mycoplasma hyopneumoniae; Chapter 4 discusses the molecular epidemiology, risk factors, transmission and prevalence of Mycoplasma hyopneumoniae, Chapter 5 discusses the clinical signs and gross lesions of Mycoplasma hyopneumoniae infection; Chapter 6 discusses immune responses against Mycoplasma infections; Chapter 7 describes the interactions of Mycoplasma hyopneumoniae with other pathogens and their economic impact; Chapter 8 discusses the diagnosis of Mycoplasma hyopneumoniae infection and its associated diseases; Chapter 9 describes the general control measures against Mycoplasma hyopneumoniae infections; Chapter 10 describes the selection and efficacy of antimicrobials against Mycoplasma hyopneumoniae infections; Chapter 11 discusses the development and efficacy of vaccines against Mycoplasma hyopneumoniae; Chapter 12 describes the eradication of Mycoplasma hyopneumoniae in pig herds; Chapter 13 describes the epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma hyorhinis and Mycoplasma hyosynoviae in pig herds and Chapter 14 discusses the epidemiology, prevalence, transmission, pathogenesis, clinical signs, diagnosis, treatment, prevention, control and economic impact of Mycoplasma suis infection in pigs.
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Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.
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Moss, Joel. Adp-Ribosylating Toxins and G Proteins: Insights into Signal Transduction (Pco-017-9). Amer Society for Microbiology, 1990.

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Ravelo, Blaise, and Zhifei Xu, eds. Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/pbcs072e.

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Xplore, IEEE. Icics-Pcm 2003: Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications & Signa. Institute of Electrical & Electronics Enginee, 2003.

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Tavares, Hermano. Assessment and Treatment of Pathological Gambling. Edited by Jon E. Grant and Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0091.

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As gambling becomes more popular, more people will be exposed to it; thus, the prevalence of and demand for gambling-related treatments are expected to increase. Pathological gambling (PG) is the most severe level of gambling compromise, characterized by unrestrained gambling to the point of financial and psychosocial harm. Classified among the impulse control disorders, PG resembles other addictive disorders. A host of scales for screening and diagnosing PG are available for both the specialist and the general practitioner. The diagnosis of PG, like that of other addictions, is based upon signs of loss of control over the target behavior (i.e., gambling), dose escalation (increasing amounts wagered to get the same excitement as in previous bets), withdrawal-like symptoms, psychosocial harm, persistent desire, and persistent betting despite the negative consequences. Its treatment requires thorough assessment of psychiatric related conditions, motivational intervention, gambling-focused psychotherapy, relapse prevention, and support for maintenance of treatment gains. Psychopharmacological tools to treat craving and gambling recurrence are an incipient but promising field.
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Ambiguity Function Analysis and Direct-Path Signal Filtering of the Digital Audio Broadcast (DAB) Waveform for Passive Coherent Location (PCL). Storming Media, 2002.

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Book chapters on the topic "PCG signals"

1

Sattar, F., F. Jin, A. Moukadem, C. Brandt, and A. Dieterlen. "Time-Scale-Based Segmentation for Degraded PCG Signals Using NMF." In Signals and Communication Technology, 179–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48331-2_8.

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Almanifi, Omair Rashed Abdulwareth, Mohd Azraai Mohd Razman, Rabiu Muazu Musa, Ahmad Fakhri Ab. Nasir, Muhammad Yusri Ismail, and Anwar P. P. Abdul Majeed. "The Classification of Heartbeat PCG Signals via Transfer Learning." In Lecture Notes in Electrical Engineering, 49–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4597-3_5.

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Athreya, A. Manoj, K. Paramesha, H. S. Avani, Pooja, and S. Madhu. "Neural Networks for Detecting Cardiac Arrhythmia from PCG Signals." In Studies in Autonomic, Data-driven and Industrial Computing, 103–15. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7771-7_9.

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Ruffo, M., M. Romano, M. Cesarelli, P. Bifulco, A. Fratini, G. Pasquariello, M. Iaccarino, and S. Iaccarino. "Comparison of software developed for FHR extraction from PCG signals." In IFMBE Proceedings, 946–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_252.

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Mubarak, Qurat-ul-ain, Muhammad Usman Akram, Arslan Shaukat, and Aneeqa Ramazan. "Quality Assessment and Classification of Heart Sounds Using PCG Signals." In Applications of Intelligent Technologies in Healthcare, 1–11. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96139-2_1.

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Gelpud, John, Silvia Castillo, Mario Jojoa, Begonya Garcia-Zapirain, Wilson Achicanoy, and David Rodrigo. "Deep Learning for Heart Sounds Classification Using Scalograms and Automatic Segmentation of PCG Signals." In Advances in Computational Intelligence, 583–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85030-2_48.

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Elgendi, Mohamed. "Photoplethysmogram Signals." In PPG Signal Analysis, 27–52. Boca Raton : Taylor & Francis, [2018]: CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-2.

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Gergely, S., M. N. Roman, and R. V. Ciupa. "Portable Complex PCG Signal Analyzer." In IFMBE Proceedings, 140–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22586-4_29.

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Elgendi, Mohamed. "Visualization of PPG Signals." In PPG Signal Analysis, 53–71. Boca Raton : Taylor & Francis, [2018]: CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-3.

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Elgendi, Mohamed. "Pre-processing of PPG Signals." In PPG Signal Analysis, 73–96. Boca Raton : Taylor & Francis, [2018]: CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-4.

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Conference papers on the topic "PCG signals"

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Baala Sundaram, Divaakar Siva, Anoushka Kapoor, Jackie Xie, Natalie C. Xu, Prissha Krishna Moorthy, Rogith Balasubramani, Suganti Shivaram, Anjani Muthyala, and Shivaram Poigai Arunachalam. "Comparison of Multiscale Frequency Characteristics of Normal Phonocardiogram With Diseased Heart States." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9090.

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Abstract Phonocardiogram (PCG) signals are electrical recording of heart sounds containing vital information of diagnostic importance. Several signal processing methods exist to characterize PCG, however suffers in terms of sensitivity and specificity in accurately discriminating normal and abnormal heart sounds. Recently, a multiscale frequency (MSF) analysis of normal PCG was reported to characterize subtle frequency content changes in PCG which can aid in differentiating normal and abnormal heart sounds. In this work, it was hypothesized that MSF can discriminate normal PCG signal compared to an artifact, PCG with extra systolic heart sounds and murmur based on their varying frequency content. Various samples of PCG with normal and abnormal heart sounds were obtained from Peter Bentley Heart Sounds Database sampled at 44.1 kHz for analysis. The signal was filtered using a 4th order Butterworth lowpass filter with cutoff frequency at 200 Hz to remove higher frequency noise and MSF estimation was performed on the filtered dataset using custom MATLAB software. Mann-Whitney test was performed for statistical significance at p < 0.05. Results indicate that MSF successfully discriminated normal and abnormal heart sounds, which can aid in PCG classification with more sophisticated analysis. Validation of this technique with larger dataset is required.
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Bashar, Md Khayrul, Samarendra Dandapat, and Itsuo Kumazawa. "Heart Abnormality Classification Using Phonocardiogram (PCG) Signals." In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2018. http://dx.doi.org/10.1109/iecbes.2018.8626627.

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Singh, Jang Bahadur, and Parveen Kumar Lehana. "Separation of PCG signal from Mixture of Speech and PCG Signals with Genetic Algorithm-Based Filter Banks." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474161.

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Siva Baala Sundaram, Divaakar, Anjani Muthyala, Rogith Balasubramani, Suganti Shivaram, Susan Karki, and Shivaram Poigai Arunachalam. "Profiling Multiscale Frequency State of Normal Phonocardiogram: Feasibility Study." In 2019 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dmd2019-3301.

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Phonocardiogram (PCG) signals contain very important information regarding the heart condition. Recently, several automatic detection algorithms have been explored to profile the characteristics of heart sounds to aid in disease diagnosis. However, many of these methods has been demonstrated only on clean signals with limited test data and variety of PCG signals that can accurately provide information of diagnostic importance with higher sensitivity and specificity. In this work, we propose to characterize the multiscale frequency state of the normal PCG signals that can aid in accurate profiling of PCG to discriminate from pathological conditions.
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Bhoi, Akash Kumar, Karma Sonam Sherpa, Jitendra Singh Tamang, Devakishore Phurailatpam, and Akhilesh Kumar Gupta. "Real time acquisition and analysis of PCG and PPG signals." In 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2015. http://dx.doi.org/10.1109/iccsp.2015.7322558.

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H. Oliveira, J., V. Ferreira, and M. Coimbra. "Can We Find Deterministic Signatures in ECG and PCG Signals?" In International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005205201840189.

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Fidan, Ugur, Naim Karasekreter, and Gulin Utebay. "Real time wireless monitoring of Ecg and Pcg signals at computer." In 2010 15th National Biomedical Engineering Meeting (BIYOMUT 2010). IEEE, 2010. http://dx.doi.org/10.1109/biyomut.2010.5479827.

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Varshney, Shivam, and Satyendra Singh. "Murmur Detection in PCG signals using DWT Entropy and Feature Clustering." In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2020. http://dx.doi.org/10.1109/conecct50063.2020.9198548.

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Manikandan, M. Sabarimalai, and S. Dandapat. "Wavelet-Based ECG and PCG Signals Compression Technique for Mobile Telemedicine." In 15th International Conference on Advanced Computing and Communications (ADCOM 2007). IEEE, 2007. http://dx.doi.org/10.1109/adcom.2007.29.

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Prasad, G. Venkata Hari, and P. Rajesh Kumar. "Performance analysis of feature selection methods for feature extracted PCG signals." In 2015 13th International Conference on Electromagnetic Interference and Compatibility (INCEMIC). IEEE, 2015. http://dx.doi.org/10.1109/incemic.2015.8055885.

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Reports on the topic "PCG signals"

1

Coplin, David L., Shulamit Manulis, and Isaac Barash. roles Hrp-dependent effector proteins and hrp gene regulation as determinants of virulence and host-specificity in Erwinia stewartii and E. herbicola pvs. gypsophilae and betae. United States Department of Agriculture, June 2005. http://dx.doi.org/10.32747/2005.7587216.bard.

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Gram-negative plant pathogenic bacteria employ specialized type-III secretion systems (TTSS) to deliver an arsenal of pathogenicity proteins directly into host cells. These secretion systems are encoded by hrp genes (for hypersensitive response and pathogenicity) and the effector proteins by so-called dsp or avr genes. The functions of effectors are to enable bacterial multiplication by damaging host cells and/or by blocking host defenses. We characterized essential hrp gene clusters in the Stewart's Wilt of maize pathogen, Pantoea stewartii subsp. stewartii (Pnss; formerly Erwinia stewartii) and the gall-forming bacterium, Pantoea agglomerans (formerly Erwinia herbicola) pvs. gypsophilae (Pag) and betae (Pab). We proposed that the virulence and host specificity of these pathogens is a function of a) the perception of specific host signals resulting in bacterial hrp gene expression and b) the action of specialized signal proteins (i.e. Hrp effectors) delivered into the plant cell. The specific objectives of the proposal were: 1) How is the expression of the hrp and effector genes regulated in response to host cell contact and the apoplastic environment? 2) What additional effector proteins are involved in pathogenicity? 3) Do the presently known Pantoea effector proteins enter host cells? 4) What host proteins interact with these effectors? We characterized the components of the hrp regulatory cascade (HrpXY ->7 HrpS ->7 HrpL ->7 hrp promoters), showed that they are conserved in both Pnss and Fag, and discovered that the regulation of the hrpS promoter (hrpSp) may be a key point in integrating apoplastic signals. We also analyzed the promoters recognized by HrpL and demonstrated the relationship between their composition and efficiency. Moreover, we showed that promoter strength can influence disease expression. In Pnss, we found that the HrpXY two-component signal system may sense the metabolic status of the bacterium and is required for full hrp gene expression in planta. In both species, acyl-homoserine lactone-mediated quorum sensing may also regulate epiphytic fitness and/or pathogenicity. A common Hrp effector protein, DspE/WtsE, is conserved and required for virulence of both species. When introduced into corn cells, Pnss WtsE protein caused water-soaked lesions. In other plants, it either caused cell death or acted as an Avr determinant. Using a yeast- two-hybrid system, WtsE was shown to interact with a number of maize signal transduction proteins that are likely to have roles in either programmed cell death or disease resistance. In Pag and Pab, we have characterized the effector proteins HsvG, HsvB and PthG. HsvG and HsvB are homologous proteins that determine host specificity of Pag and Pab on gypsophila and beet, respectively. Both possess a transcriptional activation domain that functions in yeast. PthG was found to act as an Avr determinant on multiple beet species, but was required for virulence on gypsophila. In addition, we demonstrated that PthG acts within the host cell. Additional effector genes have been characterized on the pathogenicity plasmid, pPATHₚₐg, in Pag. A screen for HrpL- regulated genes in Pnsspointed up 18 candidate effector proteins and four of these were required for full virulence. It is now well established that the virulence of Gram-negative plant pathogenic bacteria is governed by Hrp-dependent effector proteins. However; the mode of action of many effectors is still unresolved. This BARD supported research will significantly contribute to the understanding of how Hrp effectors operate in Pantoea spp. and how they control host specificity and affect symptom production. This may lead to novel approaches for genetically engineering plants resistant to a wide range of bacterial pathogens by inactivating the Hrp effectors with "plantabodies" or modifying their receptors, thereby blocking the induction of the susceptible response. Alternatively, innovative technologies could be used to interfere with the Hrp regulatory cascade by blocking a critical step or mimicking plant or quorum sensing signals.
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Vaidyanathan, P. P., and Jamal Tuqan. Oversampling PCM Techniques and Optimum Noise Shapers for Quantizing a Class of Nonbandlimited Signals,. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada323685.

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Barron, Elizabeth A. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor in the Progress of PC. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada446889.

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Wiese, Thomas E., and R. B. Klassen. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor Interactions in the Progress of PC. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada486576.

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Wiese, Thomas E., and R. B. Klassen. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor Interactions in the Progress of PC. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada486710.

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Wiese, Thomas E., and R. B. Klassen. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor Interactions in the Progress of PC. Addendum. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada512650.

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Fluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697123.bard.

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Innate immune responses in animals and plants involve receptors that recognize microbe-associated molecules. In plants, one set of this defense system is characterized by large families of TIR–nucleotide binding site–leucine-rich repeat (TIR-NBS-LRR) resistance genes. The direct interaction between plant proteins harboring the TIR domain with proteins that transmit and facilitate a signaling pathway has yet to be shown. The Arabidopsis genome encodes TIR-domain containing genes that lack NBS and LRR whose functions are unknown. Here we investigated the functional role of such protein, TLW1 (TIR LECTIN WOUNDRESPONSIVE1). The TLW1 gene encodes a protein with two domains: a TIR domain linked to a lectin-containing domain. Our specific aim in this proposal was to examine the ramifications of the TL1-glycan interaction by; A) The functional characterization of TL1 activity in the context of plant wound response and B) Examine the hypothesis that wounding induced specific polysaccharides and examine them as candidates for TL-1 interactive glycan compounds. The Weizmann group showed TLW1 transcripts are rapidly induced by wounding in a JA-independent pathway and T-DNA-tagged tlw1 mutants that lack TLW1 transcripts, fail to initiate the full systemic wound response. Transcriptome methodology analysis was set up and transcriptome analyses indicates a two-fold reduced level of JA-responsive but not JA-independent transcripts. The TIR domain of TLW1 was found to interact directly with the KAT2/PED1 gene product responsible for the final b-oxidation steps in peroxisomal-basedJA biosynthesis. To identify potential binding target(s) of TL1 in plant wound response, the CCRC group first expressed recombinant TL1 in bacterial cells and optimized conditions for the protein expression. TL1 was most highly expressed in ArcticExpress cell line. Different types of extraction buffers and extraction methods were used to prepare plant extracts for TL1 binding assay. Optimized condition for glycan labeling was determined, and 2-aminobenzamide was used to label plant extracts. Sensitivity of MALDI and LC-MS using standard glycans. THAP (2,4,6- Trihydroxyacetophenone) showed minimal background peaks at positive mode of MALDI, however, it was insensitive with a minimum detection level of 100 ng. Using LC-MS, sensitivity was highly increased enough to detect 30 pmol concentration. However, patterns of total glycans displayed no significant difference between different extraction conditions when samples were separated with Dionex ICS-2000 ion chromatography system. Transgenic plants over-expressing lectin domains were generated to obtain active lectin domain in plant cells. Insertion of the overexpression construct into the plant genome was confirmed by antibiotic selection and genomic DNA PCR. However, RT-PCR analysis was not able to detect increased level of the transcripts. Binding ability of azelaic acid to recombinant TL1. Azelaic acid was detected in GST-TL1 elution fraction, however, DHB matrix has the same mass in background signals, which needs to be further tested on other matrices. The major findings showed the importance of TLW1 in regulating wound response. The findings demonstrate completely novel and unexpected TIR domain interactions and reveal a control nexus and mechanism that contributes to the propagation of wound responses in Arabidopsis. The implications are to our understanding of the function of TIR domains and to the notion that early molecular events occur systemically within minutes of a plant sustaining a wound. A WEB site (http://genome.weizmann.ac.il/hormonometer/) was set up that enables scientists to interact with a collated plant hormone database.
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Funkenstein, Bruria, and Shaojun (Jim) Du. Interactions Between the GH-IGF axis and Myostatin in Regulating Muscle Growth in Sparus aurata. United States Department of Agriculture, March 2009. http://dx.doi.org/10.32747/2009.7696530.bard.

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Growth rate of cultured fish from hatching to commercial size is a major factor in the success of aquaculture. The normal stimulus for muscle growth in growing fish is not well understood and understanding the regulation of muscle growth in fish is of particular importance for aquaculture. Fish meat constitutes mostly of skeletal muscles and provides high value proteins in most people's diet. Unlike mammals, fish continue to grow throughout their lives, although the size fish attain, as adults, is species specific. Evidence indicates that muscle growth is regulated positively and negatively by a variety of growth and transcription factors that control both muscle cell proliferation and differentiation. In particular, growth hormone (GH), fibroblast growth factors (FGFs), insulin-like growth factors (IGFs) and transforming growth factor-13 (TGF-13) play critical roles in myogenesis during animal growth. An important advance in our understanding of muscle growth was provided by the recent discovery of the crucial functions of myostatin (MSTN) in controlling muscle growth. MSTN is a member of the TGF-13 superfamily and functions as a negative regulator of skeletal muscle growth in mammals. Studies in mammals also provided evidence for possible interactions between GH, IGFs, MSTN and the musclespecific transcription factor My oD with regards to muscle development and growth. The goal of our project was to try to clarify the role of MSTNs in Sparus aurata muscle growth and in particular determine the possible interaction between the GH-IGFaxis and MSTN in regulating muscle growth in fish. The steps to achieve this goal included: i) Determining possible relationship between changes in the expression of growth-related genes, MSTN and MyoD in muscle from slow and fast growing sea bream progeny of full-sib families and that of growth rate; ii) Testing the possible effect of over-expressing GH, IGF-I and IGF-Il on the expression of MSTN and MyoD in skeletal muscle both in vivo and in vitro; iii) Studying the regulation of the two S. aurata MSTN promoters and investigating the possible role of MyoD in this regulation. The major findings of our research can be summarized as follows: 1) Two MSTN promoters (saMSTN-1 and saMSTN-2) were isolated and characterized from S. aurata and were found to direct reporter gene activity in A204 cells. Studies were initiated to decipher the regulation of fish MSTN expression in vitro using the cloned promoters; 2) The gene coding for saMSTN-2 was cloned. Both the promoter and the first intron were found to be polymorphic. The first intron zygosity appears to be associated with growth rate; 3) Full length cDNA coding for S. aurata growth differentiation factor-l I (GDF-II), a closely related growth factor to MSTN, was cloned from S. aurata brain, and the mature peptide (C-terminal) was found to be highly conserved throughout evolution. GDF-II transcript was detected by RT -PCR analysis throughout development in S. aurata embryos and larvae, suggesting that this mRNA is the product of the embryonic genome. Transcripts for GDF-Il were detected by RT-PCR in brain, eye and spleen with highest level found in brain; 4) A novel member of the TGF-Bsuperfamily was partially cloned from S. aurata. It is highly homologous to an unidentified protein (TGF-B-like) from Tetraodon nigroviridisand is expressed in various tissues, including muscle; 5) Recombinant S. aurata GH was produced in bacteria, refolded and purified and was used in in vitro and in vivo experiments. Generally, the results of gene expression in response to GH administration in vivo depended on the nutritional state (starvation or feeding) and the time at which the fish were sacrificed after GH administration. In vitro, recombinantsaGH activated signal transduction in two fish cell lines: RTHI49 and SAFI; 6) A fibroblastic-like cell line from S. aurata (SAF-I) was characterized for its gene expression and was found to be a suitable experimental system for studies on GH-IGF and MSTN interactions; 7) The gene of the muscle-specific transcription factor Myogenin was cloned from S. aurata, its expression and promoter activity were characterized; 8) Three genes important to myofibrillogenesis were cloned from zebrafish: SmyDl, Hsp90al and skNAC. Our data suggests the existence of an interaction between the GH-IGFaxis and MSTN. This project yielded a great number of experimental tools, both DNA constructs and in vitro systems that will enable further studies on the regulation of MSTN expression and on the interactions between members of the GHIGFaxis and MSTN in regulating muscle growth in S. aurata.
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.
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