Electrocardiography ( ECG ) is a standard clinical process to record the electrical activity of the heart . It is the most available effective method for diagnosing cardiac arrhythmias . METHODS This article provides a comprehensive review of automated sleep stage scoring systems , which were created since the year 2000 . The systems were developed for Electrocardiogram ( ECG ) , Electroencephalogram ( EEG ) , Electrooculogram ( EOG ) , and a combination of signals . RESULTS Our review shows that all of these signals contain information for sleep stage scoring . Apart from the standard bands of electric oscillations found in the electroencephalogram ( EEG ) : from alpha ( 8–12 Hz ) to delta waves ( 1–4 Hz ) , synchronized firing of neural populations characterize some complex cognitive functions such as memory , attention and consciousness . In the case of electrocardiogram ( ECG ) it is usually recognized that oscillations can be understood as the limit cycle of an underlying non-linear process in heart dynamics . However , the situation is not so clear for EEG and the origin and purpose of neural oscillations are still the subject of a heated debate . The established experimental data platform generates simulated electrocardiogram , respiration and other physiological signals , which can subsequently use the signal to debug the remote electrocardiogram monitoring instrument . Conclusion : It will finally use for the subsequent electrocardiogram analysis and development of instrumentation , which provides an excellent research platform . The medical applications will vary from gynaecology , paediatrics , cardiology to typical infectious diseases for the region such as malaria and tuberculosis . The examinations will involve ultrasound examinations , electrocardiogram and blood imaging when applicable . Our medical tele-consulting tool will be used for that purpose . A framework called DiFiDOT(Digital Filter Design Optimization Tool ) is developed in this work for synthesizing the optimized filter architectures . Finally , an application for Electrocardiography(ECG ) is designed using MCM based retimed digital filters to remove the power supply interference , baseline drift and the broadband noise from the ECG signal . Electrocardiography ( ECG ) using lightweight and inexpensive ambulatory ECG devices makes it possible to monitor patients during their daily activities and can give important insight in arrhythmias and other cardiac diseases . However , everyday activities cause several kinds of motion artifacts which deteriorate the ECG quality and thus complicate both automated and manual ECG analysis . Professional groups , endorsed by individual cardiologists involved in the practice of electrocardiology , have recently issued official policy statements declaring that there is no computer program for electrocardiographic interpretation that can replace interpretation by a skilled physician , and consequently , all computer electrocardiographic interpretations require careful overreading by a physician qualified to interpret electrocardiograms ( ECGs ) . A somewhat different point of view is presented in this study , which asserts that the arguments brought forward against automated electrocardiographic interpretation may have been biased to some degree . The key issue in these deliberations is whether or not electrocardiographic analysis can be considered a laboratory test . This review elucidates factors that have hindered more widespread acceptance of automated electrocardiographic interpretation and presents possible solutions to some of the logistic problems observed . Proper measurement of signal quality is a common problem in biomedical applications , including the electrocardiogram interpretation . The need for a reliable signal-to-noise estimate raises with the common use of telemedical recordings performed in the home care conditions and interpreted automatically . Computational machine learning , especially self-enhancing algorithms , prove remarkable effectiveness in applications , including cardiovascular medicine . This review summarizes and cross-compares the current machine learning algorithms applied to electrocardiogram interpretation . In practice , continuous real-time monitoring of electrocardiograms is still difficult to realize . The irregular rhythms either slow down or increase the heart beat . This paper investigates soft computing techniques for ECG classification based on the type of arrhythmia using RR interval . Features are extracted from the time series Electro Cardiogram ( ECG ) data using Discrete Cosine Transform ( DCT ) and the distance between RR waves are computed . We focus on the main problem during the procedure of implementing the automatic diagnosis , which is illustrated with ECG clinical data . And we propose a hierarchical clustering method based on tree structure to improve the results of ECG pattern classification . The simulation results on clinical data collected by smart device such as Holter , reveal that the tree-like hierarchical clustering method can effectively detect abnormal heart beat from ECG data set . The pathophysiological processes underlying the ECG tracing demonstrate significant heart rate and the morphological pattern variations , for different or in the same patient at diverse physical/temporal conditions . Within this framework , spiking neural networks ( SNN ) may be a compelling approach to ECG pattern classification based on the individual characteristics of each patient . In this paper , we study electrophysiological dynamics in the self-organizing map SNN when the coefficients of the neuronal connectivity matrix are random variables . This study tackles the ECG classification problem by means of a methodology , which is able to enhance classification performance while simultaneously reducing the computational resources , making it specially adequate for its application in the improvement of ambulatory settings . For this purpose , the sequential forward floating search ( SFFS ) algorithm is applied with a new criterion function index based on linear discriminants . The accuracy rate is 97.50 % . Compared with the state-of-the-art methods , this method improves the accuracy of ECG automatic classification and has good generalization ability . However , the ECG- Electrocardiogram signal is mostly weak and easily interfaced by noise which leads to low accuracy by using existing methods . Therefore , in this paper , a deep learning algorithm based on gated recurrent unit for ECG automatic classification is proposed . The proposed model is firstly verified by the MIT- BIH database , and then , the experiment platform is also built to prove the feasibility and effectiveness of the presented algorithm . Classification of ECGs is an important task for proper identification of the signal which helps in suitable diagnosis of the patient . This paper proposes a new algorithm for ECG basic classification as normal or abnormal . As there are many existing methods for classification like support vector machine , neural networks , neuro-fuzzy algorithms and so on , the main objective of this work is to compare performance analysis of two selected methods , one with adaptive neuro-fuzzy algorithm as the existing method and the other with the proposed method i.e. , multimodal decision learning algorithm . VF , VT and Sinus Rhythms are characterized by distributions of threshold crossing , which can be identified , through statistical analysis techniques . Further , for the ECG classification and evaluation , the acquired signals can form each time a set of " patient vectors " , which are compared to a set of " reference vectors " , corresponding to cases , already evaluated by the medical expert , and constituting a continuously expandable knowledge base . An appropriate comparison metric allows for the retrieval of the " nearest " evaluated case , outfitting the system with a useful Decision Support Tool . Many works have been proposed for ECG data classification . Detection of ECG arrhythmias is necessary for the treatment of patients for diagnosing the heart disease at the early stage . Normal and abnormal cardiac function of the human heart can be analyzed through the application of ECG waveform processing and evaluation . Although traditionally the interpretation of these waveforms remains largely a manual effort , as computing power has increased , so too has the application of computational methods for ECG evaluation and classification . One computational method previously applied was the Artificial Neural Network but its application required the addition of signal pre-processing and feature extraction . An ECG image is classified by PNN using various feature extraction . The experimental results shows that wavelet decomposition gives a maximum accuracy compared to other feature extraction methods ( Tayel MB , El-Bouridy ME ( 2008 ) ECG images classification using artificial neural network based on several feature extraction methods . International conference on computer engineering and system pp 113–115 ) . The congestive heart failure is a major cause of concern among all types of cardiovascular problems and is attributed to imbalances in sympathetic and parasympathetic nervous systems . In this paper we propose an algorithm for detection of congestive heart failure condition using the variability of instantaneous frequency of intrinsic mode functions obtained using Hilbert-Hunag Transform of RR time series in different subjects . It is observed that the instantaneous frequency of intrinsic mode functions is a key feature that is altered quite significantly with imbalances in autonomic nervous system . The attenuation of each tone was calculated by the power ratio between the one measured on the chest and that measured at the mouth . After obtaining the average attenuation curve from the volunteers , a discrete-time equalizer was designed and applied to crackles acquired from 30 patients with fibrosis , congestive heart failure , and pneumonia . The maximum frequency and two cycle duration indices measured from these crackles were modified by the equalization . Methods for chronic heart failure detection can be utilized to act preventive , improve early diagnosis and avoid hospitalizations or even life-threatening situations , thus highly enhance the quality of patient&#x2019;s life . In this paper , we present a machine-learning method for chronic heart failure detection from heart sounds . The method consists of : filtering , segmentation , feature extraction and machine learning . Once the features are extracted , the data is ready to be used with the standard ML widgets implemented in Orange . Up to date , we evaluated the tool on five different domains : emotion recognition from speech , chronic heart failure detection form heart sounds , and species classifications from biosounds including bee , bird , and frog species . The evaluation results show high performance on all five domains . An ultra-low power frequency and duty-cycle modulated pressure-temperature sensor for use in a novel congestive heart failure monitoring system is presented . The temperature reading is used to improve the accuracy of the pressure sensor . For Atrial Fibrillation disorder , maximum f-score for the New Treatments weighing method is 0.611 , which occurs at 60 treatments . For Congestive Heart Failure disorder , maximum f-score for the New Treatments weighing method is 0.503 , which occurs at 80 treatments . Recognizing the activity of a person and measuring his/her energy expenditure is important for the management of several diseases . In the CHIRON project we aim to monitor congestive heart failure patients using wearable sensors and a smartphone . Our method uses a classifier for activity recognition constructed with machine learning . A Congestive Heart Failure Study . Spectral analysis of heart rate variability ( HRV ) has provided a very successful characterization of the autonomic influence on the heart . The authors review literature of Traditional Chinese Medicine on congestive heart failure treatment from 2005 to 2010 of CNKI database and analyze the common principle of treatment and prescription of Traditional Chinese Medicine by combining computer retrieval with artificially retrieval . The result of this study reflect the principle of treatment and law of taking medicine basically , hope to provide some ideas for clinical practice . But could previous training from the original domain alleviate this adaptation ? After having developed an NLP-based application to extract congestive heart failure treatment performance measures from echocardiogram reports ( i.e. , the source domain ) , we adapted it to a large variety of clinical documents ( i.e. , the target domain ) . We wanted to reuse the machine learning trained models from the source domain , and experimented with several popular domain adaptation approaches such as reusing the predictions from the source model , or applying a linear interpolation . In this paper we present a novel method to compare two models , using the Bipartite graphs technique , with the aim to improve the retrieval precision . This technique retrieves 3D medical models of the left ventricle in order to aid the diagnosis of Congestive Heart Failure . Results showed that the novel method improved the precision by 10 % when compared to the Similarity Function of Euclidean and Manhattan distance . Automatic QRS complex detection is important in ECG signal analysis . QRS detection methods are affected by the quality of the ECG recordings and the abnormalities in the ECG signals . Abstract The article discusses the problem of the automatic QRS complex detection in the electrocardiography signals . Among other approaches the Wavelet based algorithms are the ones of the most promising outcomes . In this paper , a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks , using fusion algorithm is proposed . R peak detection has been extensively studied and is being used to detect the QRS complex .
Electrocardiography ( ECG ) is a standard clinical process to record the electrical activity of the heart . It is the most available effective method for diagnosing cardiac arrhythmias .
METHODS This article provides a comprehensive review of automated sleep stage scoring systems , which were created since the year 2000 . The systems were developed for Electrocardiogram ( ECG ) , Electroencephalogram ( EEG ) , Electrooculogram ( EOG ) , and a combination of signals . RESULTS Our review shows that all of these signals contain information for sleep stage scoring .
Apart from the standard bands of electric oscillations found in the electroencephalogram ( EEG ) : from alpha ( 8–12 Hz ) to delta waves ( 1–4 Hz ) , synchronized firing of neural populations characterize some complex cognitive functions such as memory , attention and consciousness . In the case of electrocardiogram ( ECG ) it is usually recognized that oscillations can be understood as the limit cycle of an underlying non-linear process in heart dynamics . However , the situation is not so clear for EEG and the origin and purpose of neural oscillations are still the subject of a heated debate .
The established experimental data platform generates simulated electrocardiogram , respiration and other physiological signals , which can subsequently use the signal to debug the remote electrocardiogram monitoring instrument . Conclusion : It will finally use for the subsequent electrocardiogram analysis and development of instrumentation , which provides an excellent research platform .
The medical applications will vary from gynaecology , paediatrics , cardiology to typical infectious diseases for the region such as malaria and tuberculosis . The examinations will involve ultrasound examinations , electrocardiogram and blood imaging when applicable . Our medical tele-consulting tool will be used for that purpose .
A framework called DiFiDOT(Digital Filter Design Optimization Tool ) is developed in this work for synthesizing the optimized filter architectures . Finally , an application for Electrocardiography(ECG ) is designed using MCM based retimed digital filters to remove the power supply interference , baseline drift and the broadband noise from the ECG signal .
Electrocardiography ( ECG ) using lightweight and inexpensive ambulatory ECG devices makes it possible to monitor patients during their daily activities and can give important insight in arrhythmias and other cardiac diseases . However , everyday activities cause several kinds of motion artifacts which deteriorate the ECG quality and thus complicate both automated and manual ECG analysis .
Professional groups , endorsed by individual cardiologists involved in the practice of electrocardiology , have recently issued official policy statements declaring that there is no computer program for electrocardiographic interpretation that can replace interpretation by a skilled physician , and consequently , all computer electrocardiographic interpretations require careful overreading by a physician qualified to interpret electrocardiograms ( ECGs ) . A somewhat different point of view is presented in this study , which asserts that the arguments brought forward against automated electrocardiographic interpretation may have been biased to some degree . The key issue in these deliberations is whether or not electrocardiographic analysis can be considered a laboratory test . This review elucidates factors that have hindered more widespread acceptance of automated electrocardiographic interpretation and presents possible solutions to some of the logistic problems observed .
Proper measurement of signal quality is a common problem in biomedical applications , including the electrocardiogram interpretation . The need for a reliable signal-to-noise estimate raises with the common use of telemedical recordings performed in the home care conditions and interpreted automatically .
Computational machine learning , especially self-enhancing algorithms , prove remarkable effectiveness in applications , including cardiovascular medicine . This review summarizes and cross-compares the current machine learning algorithms applied to electrocardiogram interpretation . In practice , continuous real-time monitoring of electrocardiograms is still difficult to realize .
The irregular rhythms either slow down or increase the heart beat . This paper investigates soft computing techniques for ECG classification based on the type of arrhythmia using RR interval . Features are extracted from the time series Electro Cardiogram ( ECG ) data using Discrete Cosine Transform ( DCT ) and the distance between RR waves are computed .
We focus on the main problem during the procedure of implementing the automatic diagnosis , which is illustrated with ECG clinical data . And we propose a hierarchical clustering method based on tree structure to improve the results of ECG pattern classification . The simulation results on clinical data collected by smart device such as Holter , reveal that the tree-like hierarchical clustering method can effectively detect abnormal heart beat from ECG data set .
The pathophysiological processes underlying the ECG tracing demonstrate significant heart rate and the morphological pattern variations , for different or in the same patient at diverse physical/temporal conditions . Within this framework , spiking neural networks ( SNN ) may be a compelling approach to ECG pattern classification based on the individual characteristics of each patient . In this paper , we study electrophysiological dynamics in the self-organizing map SNN when the coefficients of the neuronal connectivity matrix are random variables .
This study tackles the ECG classification problem by means of a methodology , which is able to enhance classification performance while simultaneously reducing the computational resources , making it specially adequate for its application in the improvement of ambulatory settings . For this purpose , the sequential forward floating search ( SFFS ) algorithm is applied with a new criterion function index based on linear discriminants .
The accuracy rate is 97.50 % . Compared with the state-of-the-art methods , this method improves the accuracy of ECG automatic classification and has good generalization ability .
However , the ECG- Electrocardiogram signal is mostly weak and easily interfaced by noise which leads to low accuracy by using existing methods . Therefore , in this paper , a deep learning algorithm based on gated recurrent unit for ECG automatic classification is proposed . The proposed model is firstly verified by the MIT- BIH database , and then , the experiment platform is also built to prove the feasibility and effectiveness of the presented algorithm .
Classification of ECGs is an important task for proper identification of the signal which helps in suitable diagnosis of the patient . This paper proposes a new algorithm for ECG basic classification as normal or abnormal . As there are many existing methods for classification like support vector machine , neural networks , neuro-fuzzy algorithms and so on , the main objective of this work is to compare performance analysis of two selected methods , one with adaptive neuro-fuzzy algorithm as the existing method and the other with the proposed method i.e. , multimodal decision learning algorithm .
VF , VT and Sinus Rhythms are characterized by distributions of threshold crossing , which can be identified , through statistical analysis techniques . Further , for the ECG classification and evaluation , the acquired signals can form each time a set of " patient vectors " , which are compared to a set of " reference vectors " , corresponding to cases , already evaluated by the medical expert , and constituting a continuously expandable knowledge base . An appropriate comparison metric allows for the retrieval of the " nearest " evaluated case , outfitting the system with a useful Decision Support Tool .
Many works have been proposed for ECG data classification . Detection of ECG arrhythmias is necessary for the treatment of patients for diagnosing the heart disease at the early stage .
Normal and abnormal cardiac function of the human heart can be analyzed through the application of ECG waveform processing and evaluation . Although traditionally the interpretation of these waveforms remains largely a manual effort , as computing power has increased , so too has the application of computational methods for ECG evaluation and classification . One computational method previously applied was the Artificial Neural Network but its application required the addition of signal pre-processing and feature extraction .
An ECG image is classified by PNN using various feature extraction . The experimental results shows that wavelet decomposition gives a maximum accuracy compared to other feature extraction methods ( Tayel MB , El-Bouridy ME ( 2008 ) ECG images classification using artificial neural network based on several feature extraction methods . International conference on computer engineering and system pp 113–115 ) .
The congestive heart failure is a major cause of concern among all types of cardiovascular problems and is attributed to imbalances in sympathetic and parasympathetic nervous systems . In this paper we propose an algorithm for detection of congestive heart failure condition using the variability of instantaneous frequency of intrinsic mode functions obtained using Hilbert-Hunag Transform of RR time series in different subjects . It is observed that the instantaneous frequency of intrinsic mode functions is a key feature that is altered quite significantly with imbalances in autonomic nervous system .
The attenuation of each tone was calculated by the power ratio between the one measured on the chest and that measured at the mouth . After obtaining the average attenuation curve from the volunteers , a discrete-time equalizer was designed and applied to crackles acquired from 30 patients with fibrosis , congestive heart failure , and pneumonia . The maximum frequency and two cycle duration indices measured from these crackles were modified by the equalization .
Methods for chronic heart failure detection can be utilized to act preventive , improve early diagnosis and avoid hospitalizations or even life-threatening situations , thus highly enhance the quality of patient&#x2019;s life . In this paper , we present a machine-learning method for chronic heart failure detection from heart sounds . The method consists of : filtering , segmentation , feature extraction and machine learning .
Once the features are extracted , the data is ready to be used with the standard ML widgets implemented in Orange . Up to date , we evaluated the tool on five different domains : emotion recognition from speech , chronic heart failure detection form heart sounds , and species classifications from biosounds including bee , bird , and frog species . The evaluation results show high performance on all five domains .
An ultra-low power frequency and duty-cycle modulated pressure-temperature sensor for use in a novel congestive heart failure monitoring system is presented . The temperature reading is used to improve the accuracy of the pressure sensor .
For Atrial Fibrillation disorder , maximum f-score for the New Treatments weighing method is 0.611 , which occurs at 60 treatments . For Congestive Heart Failure disorder , maximum f-score for the New Treatments weighing method is 0.503 , which occurs at 80 treatments .
Recognizing the activity of a person and measuring his/her energy expenditure is important for the management of several diseases . In the CHIRON project we aim to monitor congestive heart failure patients using wearable sensors and a smartphone . Our method uses a classifier for activity recognition constructed with machine learning .
A Congestive Heart Failure Study . Spectral analysis of heart rate variability ( HRV ) has provided a very successful characterization of the autonomic influence on the heart .
The authors review literature of Traditional Chinese Medicine on congestive heart failure treatment from 2005 to 2010 of CNKI database and analyze the common principle of treatment and prescription of Traditional Chinese Medicine by combining computer retrieval with artificially retrieval . The result of this study reflect the principle of treatment and law of taking medicine basically , hope to provide some ideas for clinical practice .
But could previous training from the original domain alleviate this adaptation ? After having developed an NLP-based application to extract congestive heart failure treatment performance measures from echocardiogram reports ( i.e. , the source domain ) , we adapted it to a large variety of clinical documents ( i.e. , the target domain ) . We wanted to reuse the machine learning trained models from the source domain , and experimented with several popular domain adaptation approaches such as reusing the predictions from the source model , or applying a linear interpolation .
In this paper we present a novel method to compare two models , using the Bipartite graphs technique , with the aim to improve the retrieval precision . This technique retrieves 3D medical models of the left ventricle in order to aid the diagnosis of Congestive Heart Failure . Results showed that the novel method improved the precision by 10 % when compared to the Similarity Function of Euclidean and Manhattan distance .
Automatic QRS complex detection is important in ECG signal analysis . QRS detection methods are affected by the quality of the ECG recordings and the abnormalities in the ECG signals .
Abstract The article discusses the problem of the automatic QRS complex detection in the electrocardiography signals . Among other approaches the Wavelet based algorithms are the ones of the most promising outcomes .
In this paper , a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks , using fusion algorithm is proposed . R peak detection has been extensively studied and is being used to detect the QRS complex .
Features selection for arrhythmia diagnosis using Relief-F algorithm and support vector machine A review of automated sleep stage scoring based on physiological signals for the new millennia Brain oscillations in a random neural network Design of medical signal system based on MTT-BIH arrhythmia database. Broadband health care network in Brazil and Peru Framework for Digital Filter Design Optimization (DiFiDOT) using MCM Based Register Minimization Retiming for Noise Removal ECG Filters Motion Artifact Reduction in Ambulatory Electrocardiography Using Inertial Measurement Units and Kalman Filtering Will the electrocardiograph replace the electrocardiographer? Instantaneous Measurement of SNR in Electrocardiograms Based on Quasi-continuous Time-Scale Noise Modeling Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography Investigation and classification of ECG beat using Input Output Additional Weighted Feed Forward Neural Network The Mobile Medical Service Based on the Analysis of Automatic Diagnosis of Holter Electrocardiogram Data Uncertainty in Noise-Driven Steady-State Neuromorphic Network for ECG Data Classification Optimization of ECG Classification by Means of Feature Selection Electrocardiogram soft computing using hybrid deep learning CNN-ELM Research and Realization of ECG Classification based on Gated Recurrent Unit Comparative Analysis of ECG Classification Using Neuro-Fuzzy Algorithm and Multimodal Decision Learning Algorithm: ECG Classification Algorithm MED17/387: Development of a Mobile Internet-fitting Patient Monitoring System Robust System for Patient Specific Classification of ECG Signal Using PCA and Neural Network The classification of un-preprocessed ecg waveforms through the application of the hierarchical temporal memory model Classification of ECG Images Using Probabilistic Neural Network Based on Statistical Feature Analysis Investigations on instantaneous frequency variations of RR time series in intrinsic mode functions of congestive heart failure subjects Equalization of Crackle Sounds to Compensate Thorax Attenuation Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers JSI Sound - a Machine-Learning Tool in Orange for Simple Biosound Classification Ultra-low power frequency and duty-cycle modulated implantable pressure-temperature sensor Automatically extracting, ranking and visually summarizing the treatments for a disease Energy expenditure estimation with wearable accelerometers ECG-Derived Sympathetic and Parasympathetic Nervous System Dynamics: A Congestive Heart Failure Study Study on the principle of treatment, medicine and formula of Chinese Medicine in congestive heart failure based on literature review and data mining technology Improving Heart Failure Information Extraction by Domain Adaptation Using Bipartite Graphs for 3D Cardiac Model Retrieval A Robust QRS Complex Detection Algorithm Using Dynamic Thresholds Wavelet based approach to the adaptive analysis of electrocardiography signals Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently
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SDD EATIS FUSION Computer Recognition Systems 4 CBMS Intelligent Environments BAI@IJCAI BioCAS ISCAS CinC MedInfo CBMS
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