Telemedicine is gaining more and more momentum as a new approach for patients'surveillance . In many monitoring parameters , electrocardiography is the most important , especially for monitoring patients with serious cardiovascular diseases . They need to be monitored frequently in order to detect any deterioration . However , there were three important existing technologies that led to many further developments over the following 100 years . These are the stethoscope , electrocardiography , and X-ray medical imaging . Although these technologies had been described and were available to some extent when the Proceedings of the IEEE pages first appeared , they had yet to achieve the widespread use that they have today . This paper also proposes the development of fuzzy based ECG diagnosis system that assists the doctor in diagnosis . General Terms Biomedical Engineering , Electrocardiogram ( ECG ) , Image Processing Holter monitoring systems that can record heart rate and rhythm when you feel chest pain or symptoms of an irregular heartbeat ( called an arrhythmia ) and automatically perform electrocardiogram ( ECG ) signal analysis are desirable . The use of the Hilbert transform ( HT ) in the area of electrocardiogram analysis is investigated . A property of the Hilbert transform , i.e. , to form the analytic signal , was used in this thesis . Many adaptive algorithms already exist , but most of them assume white Gaussian noise as disturbance . However , for many applications such as electrocardiogram , foetus heart rate measurement , low frequency atmospheric noise , underwater acoustic noise and signal measurement in instrumentation , the impulsive noise is more common . This study presents a modified robust mixed norm ( MRMN ) adaptive filter algorithm robust to impulsive noise with higher convergence rate and lower steady state error ( SSE ) . An integrated current mode high impedance input stage designed for Electrocardiography ( ECG ) systems ( or low frequency general applications ) is presented . This feature becomes necessary when a two-electrodes ECG apparatus is used ( e.g. , in fetal ECG or heart monitoring in extreme sports ) and a good response of the system to common mode signals is required . Among many of the electrode designs used in electrocardiography ( ECG ) , concentric ring electrodes ( CREs ) are one of the most promising due to their enhanced spatial resolution . Their development has undergone a great push due to their use in recent years ; however , they are not yet widely used in clinical practice . Comparison between computed and visual time measurements showed close agreement , especially when limits of visual accuracy and beat-to-beat variations in wave duration were taken into account . The described procedure can serve as the basis for a complete electrocardiographic analysis by digital computer . Display Omitted The analysis of the surface Electrocardiogram ( ECG ) is the most extended non-invasive technique in cardiological diagnosis . The ectopic beats are heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis . These beats are very common in atrial fibrillation ( AF ) , causing important residua when ventricular activity has to be removed for atrial activity ( AA ) analysis . A significant difference between the aggregated marks scored using the ANALYSE format and the standard format was calculated ( Wilcoxon Z Score = −3.2374 ( df = 14 ) , p < 0.01 ) . This study demonstrates the clinical utility of a novel method ( ANALYSE ) to assist the learning of electrocardiogram interpretation and its association with enhanced diagnostic performance in novices . The computation time for the parameter measurements and the interpretation of an electrocardiogram by this system was about 15 seconds . The automated systems of this type would serve as a diagnostic aid for physicians in the routine electrocardiographic interpretation and the mass screening of heart diseases . Summary It is likely that in the near future the recording and interpretation of the electrocardiogram will be a completely computerized operation . Several centers are now developing computerized programs for electrocardiographic interpretation and several commercial firms are manufacturing equipment for automatic recording of miniaturized electrocardiograms . The automated equipment is quite expensive and will undoubtedly undergo considerable revision before it is completely functional . Unfortunately , Dynamic Practical Electrocardiography is not a particularly challenging teaching aid , so advanced clinicians and cardiologists will probably find it too basic . But for those starting to learn about electrocardiography or those who teach electrocardiographic reading , this is an exceptionally well thought out program with high quality images and tracings . Currently there are a lot of devices on the market that analyze ECGs , such as patient monitors , stress test systems , and Holter analysis systems , that are able to detect beats and classify arrhythmia . This paper proposes a system for ECG analysis and heartbeat classification . The proposed solution relies on a combination of machine learning algorithm and a wavelet transformation in order to maximize its performance with the minimum possible training phase . As a contribution to 2017 Physionet/CinC challenge , this work aims at the classification of different ECG heart rhythms . The importance of heart rhythm classification can not be understated , as rhythms such as atrial fibrillation have been associated with stroke , coronary artery disease and mortality . Automatic detection of heart rhythms remains a challenging task , as they can be episodic with unpredictable characteristics . In the CinC2017 challenge , the training set contains 8528 single lead short-term ECGs ( 9–90s , 300 Hz ) . Recordings are categorized into four classes namely , normal rhythm , atrial fibrillation , other rhythm , and noisy . Heart rhythm classification in this work is carried out by analyzing the atrial and ventricular activities present in the ECG . First , Noisy signals are classified using a Bagging meta-algorithm , trained on a set of features extracted from short- and long-term ECG trends . In our analysis , we consider the spectral energy of the constituent waves of the ECG as the discriminative feature . Starting with the exhaustive exploration of single heartbeat-based classification to ascertain the complexity-accuracy trade-off in different classification algorithms , we then extend our study for multiple heartbeat-based classification . We use data available in Physionet as well as samples from Southampton General Hospital Cardiology Department for our investigation . Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets . The article proposes experiments to evaluate performance of One-Against-All ( OAA ) and One-Against-One ( OAO ) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques . The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values . Automatic interpretation of electrocardiograms provides a noninvasive and inexpensive technique for analyzing the heart activity of patients with a range of cardiac conditions . We propose a method that combines locally weighted linear regression with nearest neighbor search for heartbeat detection and classification in the management of non-life-threatening arrhythmia . In the proposed method , heartbeats are detected and their features are found using the Pan – Tompkins algorithm ; then , they are classified by locally weighted linear regression on their nearest neighbors in a training set . Heartbeat classification using electrocardiogram ( ECG ) data is a vital assistive technology for wearable health solutions . We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG . Fundamental to this is a multi-layer perceptron , which incorporates these signatures to detect cardiac arrhythmia . To overcome this situation , heartbeat sound analysis is a convenient way to diagnose heart disease . Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction . Dataset-B applied in this study that contains three categories Normal , Murmur and Extra-systole heartbeat sound . Due to the lack of an effective technique for evaluating multimodal correlations , experience and intuition play a main role in present AC studies when fusing affective cues or modalities , resulting in unexpected outcomes . This study seeks to demonstrate a dynamic correlation between two such affective cues , physiological changes and spontaneous expressions , which were obtained by a combination of stereo vision based tracking and imaging photoplethysmography ( iPPG ) , with a designed protocol involving 20 healthy subjects . The two cues obtained were sampled into a Statistical Association Space ( SAS ) to evaluate their dynamic correlation . A Novel VLIW-Based Chip Multiprocessor for Accelerating Biomedical Image Processing Applications . We discuss BioThreads , a novel , configurable , extensible system-on-chip multiprocessor and its use in accelerating biomedical signal processing applications such as imaging photoplethysmography ( IPPG ) . BioThreads is derived from the LE1 open-source VLIW chip multiprocessor and efficiently handles instruction , data and thread-level parallelism . ﻿ This thesis investigates the feasibility of using a set of non-invasive biomedical signals to monitor respiration . The signals of interest being the electrocardiogram ( EGG ) , photoplethysmography ( PPG ) and impedance plethysmography ( IP ) signals . The work has two main aims ; the first being to estimate breathing rates from the signals , the second being to detect apnoeas from the signals . Abstract Photoplethysmography ( PPG ) is a well-studied and promising technique to detect heart rate ( HR ) using cheap , non-invasive , wrist-wearable sensors that sense the amount of light reflected by the skin , related to the blood flow beneath . Still , the main issue is the high sensitivity to motion , which produces severe artifacts in the signal , often impeding accurate HR tracking . Abstract Photoplethysmography ( PPG ) as a non-invasive and low-cost technique plays a significant role in wearable Internet-of-Things based health monitoring systems , enabling continuous health and well-being data collection . As PPG monitoring is relatively simple , non-invasive , and convenient , it is widely used in a variety of wearable devices ( e.g. , smart bands , smart rings , smartphones ) to acquire different vital signs such as heart rate and pulse rate variability . We report progresses in the development of a processing pipeline intended for photoplethysmogram ( PPG ) based on the use of silicon photomultipliers ( SiPMs ) detectors . Such probe sensors offer relevant advantages in terms of single-photon sensitivity and high internal gain for relatively low reverse bias . Video photoplethysmography ( videoPPG ) has emerged as area of great interest thanks to the possibility of remotely assessment of cardiovascular parameters , as heart rate ( HR ) , respiration rate ( RR ) and heart rate variability ( HRV ) . The present article proposes a fully automated method based on chrominance model , that selects for each subject the best region of interest ( ROI ) to detect and evaluate the accuracy of beat detection and interbeat intervals ( IBI ) measurements . However , many effective face detection methods still stay in grayscale images . Nowadays , photoplethysmographic imaging ( PPGi ) for cardiovascular and hemodynamic analysis has become an attractive research area and pulsatile signal extracted from skin surface can be obtained using a digital camera under the condition of the ambient light . In this paper , we introduce a new approach of face detection based on the PPGi technology . A method is presented for the detection of ventricular fibrillation using binary sequences derived from the surface electrocardiogram . The binary sequences are used to obtain threshold crossing interval and Lempel-Ziv complexity measurements which together form the inputs to a neural network classifier . Defibrillator is one of the most advanced radio electronic medical devices allowing considerable reduction of deaths caused by ventricular defibrillation . In this paper problems of introduction of automated external defibrillators for application in Ukraine are considered by the author . Much of the research into the mechanisms of ventricular fibrillation ( VF ) employs high-resolution mapping of electrical activation and recovery patterns . We previously developed a method for analyzing electrically mapped VF patterns that was based on identifying individual VF wavefronts . Wearable defibrillators and subcutaneous ICDs are ignored . Chapter 4 on ventricular fibrillation detection was written by coauthor Eric Laciar Leber beginning with performance definitions for sensitivity , specificity , positive predictivity and receiver operating curves . The seven VF detection algorithms described have not been used in real devices because they exceed real-time computational limits or have medically unacceptable performance . They can be identified by means of surface ECG leads . The main goal of this work is to provide a signal classification based on ECG signal waveform in the time-frequency domain especially targeted to Ventricular Fibrillation detection . The use of a classifier based on a Boltzmann network is proposed . Alfonso VX , Tompkins WJ . Detecting Ventricular Fibrillation . Selecting the Appropriate Time-Frequency Analysis Tool for the Application . In this paper , we will present a framework of AED system . There are two crucial components should be elaborately designed , the hardware system and the algorithm of detecting ventricular fibrillation . In this paper , we will present the design of hardware system and a new algorithm which could discriminate ventricular fibrillation ( VF ) from other unshockable rhythm through the measurement of Sample Entropy ( SampEn ) . The highest overall classification accuracy of 83.75%was achieved with noise width of 0.1 . Thus , the ensemble empirical mode decomposition of PPG signals was successfully used for assessment of ventricular fibrillation and further modifications with the parameters and pre-processing techniques may be done to improve classification accuracy based on this feature . Based on the event detection model , middleware can acquire changing information real-timely . We have successfully applied our middleware for ventricular fibrillation monitoring . Finally , experiments prove that our on-demand deployment model outperforms other selected models both in total latency and throughput , especially in dynamic environments . A three-dimensional cellular automata model of activation wavefront propagation in ventricular cardiac tissue was created . The model geometry was obtained from magnetic resonance ( MR ) images of a canine heart which was used in a ventricular fibrillation ( VF ) study . Propagation within the simulated and real hearts was compared .
Telemedicine is gaining more and more momentum as a new approach for patients'surveillance . In many monitoring parameters , electrocardiography is the most important , especially for monitoring patients with serious cardiovascular diseases . They need to be monitored frequently in order to detect any deterioration .
However , there were three important existing technologies that led to many further developments over the following 100 years . These are the stethoscope , electrocardiography , and X-ray medical imaging . Although these technologies had been described and were available to some extent when the Proceedings of the IEEE pages first appeared , they had yet to achieve the widespread use that they have today .
This paper also proposes the development of fuzzy based ECG diagnosis system that assists the doctor in diagnosis . General Terms Biomedical Engineering , Electrocardiogram ( ECG ) , Image Processing
Holter monitoring systems that can record heart rate and rhythm when you feel chest pain or symptoms of an irregular heartbeat ( called an arrhythmia ) and automatically perform electrocardiogram ( ECG ) signal analysis are desirable . The use of the Hilbert transform ( HT ) in the area of electrocardiogram analysis is investigated . A property of the Hilbert transform , i.e. , to form the analytic signal , was used in this thesis .
Many adaptive algorithms already exist , but most of them assume white Gaussian noise as disturbance . However , for many applications such as electrocardiogram , foetus heart rate measurement , low frequency atmospheric noise , underwater acoustic noise and signal measurement in instrumentation , the impulsive noise is more common . This study presents a modified robust mixed norm ( MRMN ) adaptive filter algorithm robust to impulsive noise with higher convergence rate and lower steady state error ( SSE ) .
An integrated current mode high impedance input stage designed for Electrocardiography ( ECG ) systems ( or low frequency general applications ) is presented . This feature becomes necessary when a two-electrodes ECG apparatus is used ( e.g. , in fetal ECG or heart monitoring in extreme sports ) and a good response of the system to common mode signals is required .
Among many of the electrode designs used in electrocardiography ( ECG ) , concentric ring electrodes ( CREs ) are one of the most promising due to their enhanced spatial resolution . Their development has undergone a great push due to their use in recent years ; however , they are not yet widely used in clinical practice .
Comparison between computed and visual time measurements showed close agreement , especially when limits of visual accuracy and beat-to-beat variations in wave duration were taken into account . The described procedure can serve as the basis for a complete electrocardiographic analysis by digital computer .
Display Omitted The analysis of the surface Electrocardiogram ( ECG ) is the most extended non-invasive technique in cardiological diagnosis . The ectopic beats are heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis . These beats are very common in atrial fibrillation ( AF ) , causing important residua when ventricular activity has to be removed for atrial activity ( AA ) analysis .
A significant difference between the aggregated marks scored using the ANALYSE format and the standard format was calculated ( Wilcoxon Z Score = −3.2374 ( df = 14 ) , p < 0.01 ) . This study demonstrates the clinical utility of a novel method ( ANALYSE ) to assist the learning of electrocardiogram interpretation and its association with enhanced diagnostic performance in novices .
The computation time for the parameter measurements and the interpretation of an electrocardiogram by this system was about 15 seconds . The automated systems of this type would serve as a diagnostic aid for physicians in the routine electrocardiographic interpretation and the mass screening of heart diseases .
Summary It is likely that in the near future the recording and interpretation of the electrocardiogram will be a completely computerized operation . Several centers are now developing computerized programs for electrocardiographic interpretation and several commercial firms are manufacturing equipment for automatic recording of miniaturized electrocardiograms . The automated equipment is quite expensive and will undoubtedly undergo considerable revision before it is completely functional .
Unfortunately , Dynamic Practical Electrocardiography is not a particularly challenging teaching aid , so advanced clinicians and cardiologists will probably find it too basic . But for those starting to learn about electrocardiography or those who teach electrocardiographic reading , this is an exceptionally well thought out program with high quality images and tracings .
Currently there are a lot of devices on the market that analyze ECGs , such as patient monitors , stress test systems , and Holter analysis systems , that are able to detect beats and classify arrhythmia . This paper proposes a system for ECG analysis and heartbeat classification . The proposed solution relies on a combination of machine learning algorithm and a wavelet transformation in order to maximize its performance with the minimum possible training phase .
As a contribution to 2017 Physionet/CinC challenge , this work aims at the classification of different ECG heart rhythms . The importance of heart rhythm classification can not be understated , as rhythms such as atrial fibrillation have been associated with stroke , coronary artery disease and mortality . Automatic detection of heart rhythms remains a challenging task , as they can be episodic with unpredictable characteristics . In the CinC2017 challenge , the training set contains 8528 single lead short-term ECGs ( 9–90s , 300 Hz ) . Recordings are categorized into four classes namely , normal rhythm , atrial fibrillation , other rhythm , and noisy . Heart rhythm classification in this work is carried out by analyzing the atrial and ventricular activities present in the ECG . First , Noisy signals are classified using a Bagging meta-algorithm , trained on a set of features extracted from short- and long-term ECG trends .
In our analysis , we consider the spectral energy of the constituent waves of the ECG as the discriminative feature . Starting with the exhaustive exploration of single heartbeat-based classification to ascertain the complexity-accuracy trade-off in different classification algorithms , we then extend our study for multiple heartbeat-based classification . We use data available in Physionet as well as samples from Southampton General Hospital Cardiology Department for our investigation .
Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets . The article proposes experiments to evaluate performance of One-Against-All ( OAA ) and One-Against-One ( OAO ) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques . The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values .
Automatic interpretation of electrocardiograms provides a noninvasive and inexpensive technique for analyzing the heart activity of patients with a range of cardiac conditions . We propose a method that combines locally weighted linear regression with nearest neighbor search for heartbeat detection and classification in the management of non-life-threatening arrhythmia . In the proposed method , heartbeats are detected and their features are found using the Pan – Tompkins algorithm ; then , they are classified by locally weighted linear regression on their nearest neighbors in a training set .
Heartbeat classification using electrocardiogram ( ECG ) data is a vital assistive technology for wearable health solutions . We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG . Fundamental to this is a multi-layer perceptron , which incorporates these signatures to detect cardiac arrhythmia .
To overcome this situation , heartbeat sound analysis is a convenient way to diagnose heart disease . Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction . Dataset-B applied in this study that contains three categories Normal , Murmur and Extra-systole heartbeat sound .
Due to the lack of an effective technique for evaluating multimodal correlations , experience and intuition play a main role in present AC studies when fusing affective cues or modalities , resulting in unexpected outcomes . This study seeks to demonstrate a dynamic correlation between two such affective cues , physiological changes and spontaneous expressions , which were obtained by a combination of stereo vision based tracking and imaging photoplethysmography ( iPPG ) , with a designed protocol involving 20 healthy subjects . The two cues obtained were sampled into a Statistical Association Space ( SAS ) to evaluate their dynamic correlation .
A Novel VLIW-Based Chip Multiprocessor for Accelerating Biomedical Image Processing Applications . We discuss BioThreads , a novel , configurable , extensible system-on-chip multiprocessor and its use in accelerating biomedical signal processing applications such as imaging photoplethysmography ( IPPG ) . BioThreads is derived from the LE1 open-source VLIW chip multiprocessor and efficiently handles instruction , data and thread-level parallelism .
﻿ This thesis investigates the feasibility of using a set of non-invasive biomedical signals to monitor respiration . The signals of interest being the electrocardiogram ( EGG ) , photoplethysmography ( PPG ) and impedance plethysmography ( IP ) signals . The work has two main aims ; the first being to estimate breathing rates from the signals , the second being to detect apnoeas from the signals .
Abstract Photoplethysmography ( PPG ) is a well-studied and promising technique to detect heart rate ( HR ) using cheap , non-invasive , wrist-wearable sensors that sense the amount of light reflected by the skin , related to the blood flow beneath . Still , the main issue is the high sensitivity to motion , which produces severe artifacts in the signal , often impeding accurate HR tracking .
Abstract Photoplethysmography ( PPG ) as a non-invasive and low-cost technique plays a significant role in wearable Internet-of-Things based health monitoring systems , enabling continuous health and well-being data collection . As PPG monitoring is relatively simple , non-invasive , and convenient , it is widely used in a variety of wearable devices ( e.g. , smart bands , smart rings , smartphones ) to acquire different vital signs such as heart rate and pulse rate variability .
We report progresses in the development of a processing pipeline intended for photoplethysmogram ( PPG ) based on the use of silicon photomultipliers ( SiPMs ) detectors . Such probe sensors offer relevant advantages in terms of single-photon sensitivity and high internal gain for relatively low reverse bias .
Video photoplethysmography ( videoPPG ) has emerged as area of great interest thanks to the possibility of remotely assessment of cardiovascular parameters , as heart rate ( HR ) , respiration rate ( RR ) and heart rate variability ( HRV ) . The present article proposes a fully automated method based on chrominance model , that selects for each subject the best region of interest ( ROI ) to detect and evaluate the accuracy of beat detection and interbeat intervals ( IBI ) measurements .
However , many effective face detection methods still stay in grayscale images . Nowadays , photoplethysmographic imaging ( PPGi ) for cardiovascular and hemodynamic analysis has become an attractive research area and pulsatile signal extracted from skin surface can be obtained using a digital camera under the condition of the ambient light . In this paper , we introduce a new approach of face detection based on the PPGi technology .
A method is presented for the detection of ventricular fibrillation using binary sequences derived from the surface electrocardiogram . The binary sequences are used to obtain threshold crossing interval and Lempel-Ziv complexity measurements which together form the inputs to a neural network classifier .
Defibrillator is one of the most advanced radio electronic medical devices allowing considerable reduction of deaths caused by ventricular defibrillation . In this paper problems of introduction of automated external defibrillators for application in Ukraine are considered by the author .
Much of the research into the mechanisms of ventricular fibrillation ( VF ) employs high-resolution mapping of electrical activation and recovery patterns . We previously developed a method for analyzing electrically mapped VF patterns that was based on identifying individual VF wavefronts .
Wearable defibrillators and subcutaneous ICDs are ignored . Chapter 4 on ventricular fibrillation detection was written by coauthor Eric Laciar Leber beginning with performance definitions for sensitivity , specificity , positive predictivity and receiver operating curves . The seven VF detection algorithms described have not been used in real devices because they exceed real-time computational limits or have medically unacceptable performance .
They can be identified by means of surface ECG leads . The main goal of this work is to provide a signal classification based on ECG signal waveform in the time-frequency domain especially targeted to Ventricular Fibrillation detection . The use of a classifier based on a Boltzmann network is proposed .
Alfonso VX , Tompkins WJ . Detecting Ventricular Fibrillation . Selecting the Appropriate Time-Frequency Analysis Tool for the Application .
In this paper , we will present a framework of AED system . There are two crucial components should be elaborately designed , the hardware system and the algorithm of detecting ventricular fibrillation . In this paper , we will present the design of hardware system and a new algorithm which could discriminate ventricular fibrillation ( VF ) from other unshockable rhythm through the measurement of Sample Entropy ( SampEn ) .
The highest overall classification accuracy of 83.75%was achieved with noise width of 0.1 . Thus , the ensemble empirical mode decomposition of PPG signals was successfully used for assessment of ventricular fibrillation and further modifications with the parameters and pre-processing techniques may be done to improve classification accuracy based on this feature .
Based on the event detection model , middleware can acquire changing information real-timely . We have successfully applied our middleware for ventricular fibrillation monitoring . Finally , experiments prove that our on-demand deployment model outperforms other selected models both in total latency and throughput , especially in dynamic environments .
A three-dimensional cellular automata model of activation wavefront propagation in ventricular cardiac tissue was created . The model geometry was obtained from magnetic resonance ( MR ) images of a canine heart which was used in a ventricular fibrillation ( VF ) study . Propagation within the simulated and real hearts was compared .
[Design of a cardiac monitoring system using mobile communications]. Advances in Medical Devices and Medical Electronics ECG Paper Records Digitization through Image Processing Techniques Numerical implementation of the Hilbert transform FPGA implementation of hardware efficient adaptive filter robust to impulsive noise A CCII-Based High Impedance Input Stage for Biomedical Applications Textile Concentric Ring Electrodes for ECG Recording Based on Screen-Printing Technology Automatic Recognition of Electrocardiographic Waves by Digital Computer An efficient method for ECG beat classification and correction of ectopic beats A clinical decision support tool to assist with the interpretation of the 12-lead electrocardiogram On-line computer diagnosis of arrhythmias on ECG using by small scale digital computer system. A modified ECG system for medium-sized hospitals. Dynamic Practical Electrocardiography: A Virtual Clinic and Classroom ECG Analysis and Heartbeat Classification Based on Shallow Neural Networks Heart rhythm classification using short-term ECG atrial and ventricular activity analysis Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems Multiclass Support Vector Machines for Classification of ECG Data with Missing Values Nearest neighbor search with locally weighted linear regression for heartbeat classification Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG Heartbeat Sound Signal Classification Using Deep Learning A Study of the Dynamic Relation between Physiological Changes and Spontaneous Expressions BioThreads: A Novel VLIW-Based Chip Multiprocessor for Accelerating Biomedical Image Processing Applications Signal processing methods for non-invasive respiration monitoring Reduced complexity algorithm for heart rate monitoring from PPG signals using automatic activity intensity classifier A Real-time PPG Quality Assessment Approach for Healthcare Internet-of-Things Progresses towards a processing pipeline in photoplethysmogram (PPG) based on SiPMs Assessment of beat-to-beat heart rate detection method using a camera as contactless sensor A New Approach for Face Detection Based on Photoplethysmographic Imaging Detection of ventricular fibrillation by sequential hypothesis testing of binary sequences Problems of introduction of automated external defibrillators for application in Ukraine. Defibrillator is one of the most advanced radio electronic medical devices allowing considerable reduction of deaths caused by ventricular defibrillation Combined phase singularity and wavefront analysis for optical maps of ventricular fibrillation Review of "Cardiac Fibrillation-Defibrillation: Clinical and Engineering Aspects", by Max E. Valentinuzzi ECG Analysis for Ventricular Fibrillation Detection Using a Boltzmann Network Development of a Multi-purpose CCU Monitoring System Adapted to Cardiac Arrest Risk Assessment A new ECG-based Automated External Defibrillator system Ensemble Empirical Mode Decomposition of Photoplethysmogram Signals for Assessment of Ventricular Fibrillation On-demand deployment for IoT applications A Cellular Automata Three-dimensional Model Of Ventricular Cardiac Activation
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2006 2012 2012 2006 2017 2011 2018 1961 2016 2019 1969 1969 2000 2019 2017 2013 2015 2018 2018 2019 2017 2012 2002 2019 2019 2017 2016 2015 2007 2010 2004 2011 2015 2000 2010 2018 2020 1991
MOCAST CinC CHASE ANT/EDI40 ECCTD EMBC HIS AMIA
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