The forward problem of electrocardiography refers to the calculation of the body surface potentials starting from either equivalent current dipoles that represent the heart 's electrical activity or from potentials on the epicardium . This paper reviews the numerical methods used to solve the forward problem of electrocardiography . The use of preamplified electrodes in electrocardiography ( ECG ) or electroencephalography ( EEG ) has a number of benefits , most importantly , the noise reduction through preamplification and the interference reduction through buffering ( Fernandez and Pallas-Areny , 1997 ) . Normally , preamplified electrodes are not used because the mismatch in amplification due to component tolerances leads to a reduced common mode rejection ration ( CMRR ) ( Pallas-Areny and Webster , 1991 ) . Traditionally , pulse arrival time ( PAT ) contains the pre-ejection period ( PEP ) and is prone to smooth muscle ( SM ) contraction . Besides , the implementation of electrocardiogram ( ECG ) requires many measurement sites , which is inconvenient and obtrusive . In this paper , peripheral pulse transit time ( PPTT ) is proposed for NCBP estimation . It is shown that this technique is better than the existing methods , and the estimates obtained by this technique are more meaningful in evaluating the quality of feature extractors . Computer simulation as well as application to electrocardiogram analysis are used to demonstrate this technique . The embodiment of the invention provides an electrocardiosignal baseline recognizer which comprises a data input module , an FIFO ( First In First Out ) buffer module , a central control module , a baseline recognition module and a data output module , and the embodiment of the invention further provides an electrocardiosignal baseline recognition method . Through directly recognizing the baseline position , the analytical calculation to the subsequent electrocardiogram analysis is carried out directly based on the baseline position , the baseline drift rejection technology is not required to perform pretreatment , so that the error caused by the application of the baseline drift rejection technology can be effectively reduced , and meanwhile , the subsequent electrocardiogram analysis is simpler and more convenient . BACKGROUND AND OBJECTIVE Myocardial infarction ( MI ) is one of the most threatening cardiovascular diseases for human beings , which can be diagnosed by electrocardiogram ( ECG ) . Automated detection methods based on ECG focus on extracting handcrafted features . We present algorithm for Heart Rate detection based on Short-Term Autocorrelation Center Clipping method . This algorithm is dedicated for biological signal detection , electrocardiogram , in noisy environment with lot of artifacts . Using this algorithm is also possible detect the R pointers in the PQRST complex of the ECG signal . This paper describes a capacitively-coupled chopper instrumentation amplifier for use in electrocardiography ( ECG ) . The amplifier 's gain is accurately defined by a capacitive feedback network , while a DC servo loop rejects the DC offset generated by the electrode-tissue interface . Ectopic beats are early 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 . Abstract : The thesis describes the development and results of a real-time computer system for electrocardiographic analysis using the PDP-15 computer . The system was developed using algorithms that were previously developed at the Air Force Institute of Technology and the Cox Heart Institute . When using the available m-health systems , ECG data for a small duration is recorded and sent to a server for processing and arrhythmia detection . Since arrhythmia occurrence is not so frequent in early stages , a need is felt to develop a real time and continuous arrhythmia monitoring system on the phone itself . The method has system . A three-channel 24-hour recorded ECG can be completely analyzed within 5 minutes for re orting the heart rate variations , ST level analysis , teart beat classifications and arrhythmia analysis . also been implemented on a P e -486 based computer Results presented in histogram and cumulative histogram plots showed that the signal quality can be accurately assessed using this new method . Of the total 44 non-paced records , the new method identified 22 records where one of the two leads had much better signal quality than the other lead When this information was used for arrhythmia analysis , the averaged PVC false positive rate was 0.47 % for leads that were selected by the new method , and 2.56 % for leads that were not selected These results clearly showed that the new signal quality measure developed can be used to accurately assess the ECG signal quality and can be incorporated easily into existing arrhythmia algorithms for performance improvement . Particularly , my special thanks go to JHRS members boasting the highest number of abstracts submitted for each year׳s scientific session ; their papers are also academically invaluable . Amidst the situation where a huge gap still exists between countries in the Asia-Pacific region in terms of research and treatment in the field of arrhythmia , our goal is to help such advanced countries as Japan take a step forward to a globally leading level in this sphere , while assisting undeveloped countries in creating the relevant infrastructure to have the arena of arrhythmia established as an academic field and as a practical care and treatment for patients as well . In order to achieve this goal , I think the academic exchange through “ Journal of Arrhythmia”—the official journal of the JHRS and the APHRS — is significant . But it is difficult to utilize morphological information and temporal information simultaneously when dealing with single ECG heartbeats , because RR interval is not contained in a single short heartbeat . Therefore , to handle this problems , a novel Multi-information Fusion Convolutional Bidirectional Recurrent Neural Network ( MF-CBRNN ) is proposed for arrhythmia automatic detection . METHODS . Smartphone or wearable sensor extracted photoplethysmogram ( PPG ) is the sole physiological signal that is considered to characterize the cardiac anomalous events . Our main novelty is to precisely detect and remove the motion artifacts in PPG signals and to ensure accuracy in arrhythmia condition detection , specifically to reduce the false negative alarms . We establish the efficacy of proposed solution , iCarMa by large set of experiments with field-collected and MIT-Physionet PPG signals . This promises to improve diagnosis accuracy , but the large data volume presents new challenges for cardiologists . This paper introduces ECGLens , an interactive system for arrhythmia detection and analysis using large-scale ECG data . Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network , an outlier detection algorithm , and a set of rich interaction techniques . In fact , electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues . In this paper , we focus on the arrhythmia detection problem . Specifically , our objective is to address the problem of distributed processing considering big data generated by electrocardiogram ( ECG ) signals in order to carry out pre-processing analysis . Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential , commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist where noise remains an unsolved problem . Here , we develop a multi-task deep learning method to assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation ( AF ) . We train our algorithm on over one million simulated unlabeled physiological signals and fine-tune on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices . It is noteworthy that our model can conduct these procedures in a single stream and learn all network parameters jointly in an end-to-end manner . We validated the effectiveness of our method using the public data sets of MIMIC-III and PhysioNet challenge 2012 by comparing with and outperforming other state-of-the-art methods for mortality prediction considered in our experiments . In addition , we identified the behavior of the model that well represented the uncertainties for the imputed estimates , which showed a high correlation between the uncertainties and mean absolute error ( MAE ) scores for imputation . We also try to conclude , from the two scoring systems presented , which predicts better the situations described . Regarding the results , we concluded that the PULP scoring allows a better mortality prediction achieving , in this case , above 90 % accuracy , however , the results may be inconclusive due to the lack of patients who died in the dataset used . Regarding the complications , we concluded that , on the other hand , the Boey system achieves better results leading to a better prediction when it comes to predicting patients ’ state complication . However , mortality prediction for ICU patients has typically been conducted by building one common predictive model for all the diseases . In this paper , we incorporate disease-specific contexts into mortality modeling by formulating the mortality prediction problem as a multi-task learning problem in which a task corresponds to a disease . Our method effectively integrates medical domain knowledge relating to the similarity among diseases and the similarity among Electronic Health Records ( EHRs ) into a data-driven approach by incorporating graph Laplacians into the regularization term to encode these similarities . Using the PAG we develop an algorithm that determines an interventional distribution that is stable to the declared shifts ; this subsumes existing approaches which find stable feature sets that are less accurate . We apply I-SPEC to a mortality prediction problem to show it can learn a model that is robust to shifts without needing upfront knowledge of the full causal DAG . The goal of this study is to conduct a systematic comparative study of different ML algorithms for several predictive modeling problems in urgent care . Design We assess the performance of 4 benchmark prediction tasks ( eg mortality and prediction , differential diagnostics , and disease marker discovery ) using medical histories , physiological time-series , and demographics data from the Medical Information Mart for Intensive Care ( MIMIC-III ) database . Measurements For each given task , performance was estimated using standard measures including the area under the receiver operating characteristic ( AUC ) curve , F-1 score , sensitivity , and specificity . Keynote Papers : Analysis of Clustered Multivariate Data from Developmental Toxicity Studies Wavelets in Statistics : Some Recent Developments.- Invited Papers : Mortality Pattern Prediction in Worker Cohorts Design Algorithms for Correlated Data (Co)Variance Structures for Linear Models in the Analysis of Plant Improvement Data Optimal Scaling Methods for Graphical Display of Multivariate Data Computer-assisted Statistics Teaching in Network Environments Modelling Bacterial Genomes using Hidden Markov Models MCMC Specifics for Latent Variable Methods Exploritory versus Decision Trees Exploring Time Series using Semi- and Nonparametric Methods Time Series Forecasting by Principal Component Methods.- Contributed Papers : A Simulation Study of Indirect Prognostic Classification Model Search : an Overview Speeding up the Computation of the Least Quartile Difference Estimator Piece-wise Detection of Spinal Scoliosis A Comparison of Recent EM Accelerators with Item Response Theory Grobner Basis Methods in Polynomial Modelling Analysis of High Dimensional Data from Intensive Care Medicine Fitting Non-Gaussian Time Series Models Using Singular Value Decomposition in Non-Linear Regression A Modelling Approach for Bandwidth Selection in Kernel Density Estimation A New Method for Cross-Classfication Analysis of Contigency Data Tables Bayesian Analysis of Overdispersed Count Data with Application to Teletraffic Monitoring An Alternative Pruning Method Based on the Impurity-Complexity Measure Bayesian Inference for Mixture : The final classification , considering mortality as the positive class , resulted in an accuracy of 91.56 % , sensitivity of 90.59 % , precision of 86.52 % and F-1-score of 88.50 % . The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose . Moreover , the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs . Tools for signal filtration , interactive intervals and segment measurements and automatic R wave detection are implemented within the application and are also presented in this paper . The application allows analysis of heart rate variability , a relatively new method for evaluating proper autonomic nervous system ( ANS ) functioning . Analysis of heart rate variability ( HRV ) is based on time-domain methods ( SDNN , RMSSD , pNN50 , HR and mean HR ) and methods of nonlinear dynamics ( ApEn , 0V% , 1V% , 2LV% , 2UV% , Porta index , Guzik index , Ehlers index ) . Holter recorders are currently changing their typical arrhythmia detection focus towards additional ECG evaluation objectives like ST-T-segment or heart rate variability analysis . Such estimations are only applicable when they are related to normal sinus rhythm excitations . In this thesis , design and implementation of a new PC-based heart rate variability recording system with respiration is described . The respiration rate is detected by a thermocouple placed in a nasal tube , whereas a single-channel electrocardiogram ( ECG ) signal is recorded for heart rate variability analysis in the system . The PC-based data acquisition part , which was designed in compliance to patient safety standards , has a Universal Serial Bus ( USB ) interface . Contact measurements of the cardiac pulse by using whether conventional electrocardiogram equipment or commercially-available pulse oximetry sensors would cause inconvenience for patients . Therefore , a novel robust non-contact technique is developed for the evaluation of heart rate variation . Image-based heart rate measurement based on multiple regression method is proposed . Commercially available pulse oximetry sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can be painful to use . Therefore , a novel robust non-contact technique is developed for the evaluation of heart rate variation . According to the periodic variation of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as heartbeats cause changes to blood volume in the blood vessels in the face , a reflectance signal is decomposed from consecutive frames of the green channel of the facial region . HRV ( Heart Rate Variability ) monitoring is a relatively new method for measuring the effects of stress on the human body . Through monitoring HRV data , doctors can quantify the health status of the patient . The device was evaluated under three different scenarios , each one designed to test the different ExG modalities . Initial tests showed promising results and allowed for ( 1 ) steady-state visually evoked potentials to be accurately measured from EEG , ( 2 ) heart rate variability measurements to discriminate between different affective videos , and ( 3 ) EOG measurements to monitor gaze direction and eye blinks , all while users were mobile . Being able to accurately monitor signals from the autonomic and central nervous systems in an unobtrusive and portable manner is an important step for instrumental QoE assessment of emerging VR/AR applications . Non-contact heart rate and heart rate variability measurements have applications in healthcare and affective computing . Recently , a system utilizing a five-band camera ( RGBCO : red , green , blue , cyan , orange ) was proposed , and shown to improve both remote measurement of heart rate and heart rate variability over an RGB camera . The current paper proposes an application of methods from visual analytics to time-frequency energy distribution in ECG signals , with the goal of studying heart rate variability from the main ECG band corresponding to heart rate . Such an approach can complement standard time- and frequency-domain techniques for heart rate variability assessment . Multiresolution wavelet entropy was computed from continuous wavelet transforms using windows of user-specified length and overlap . This thesis suggests that nanotechnological devices may serve as a potential alternative to other heart rate measuring technology . Limitations in detecting minor heart rate changes between different driving conditions and in heart rate variability measuring were also acknowledged . Heart rate variability and motion artifact are two major obstacles to the photoplethysmographic ( PPG ) signal analysis , which is a very promising tool to derive useful information about the hemodynamics as well as autonomic nerve system . This paper suggests employing a truncation/extrapolation method on the PPG signal or its first order derivative to overcome the heart rate variability problem while extract a refined mean PPG pulse waveform . Meanwhile , the cross-correlation detection method is employed to remove the motion artifacts .
The forward problem of electrocardiography refers to the calculation of the body surface potentials starting from either equivalent current dipoles that represent the heart 's electrical activity or from potentials on the epicardium . This paper reviews the numerical methods used to solve the forward problem of electrocardiography .
The use of preamplified electrodes in electrocardiography ( ECG ) or electroencephalography ( EEG ) has a number of benefits , most importantly , the noise reduction through preamplification and the interference reduction through buffering ( Fernandez and Pallas-Areny , 1997 ) . Normally , preamplified electrodes are not used because the mismatch in amplification due to component tolerances leads to a reduced common mode rejection ration ( CMRR ) ( Pallas-Areny and Webster , 1991 ) .
Traditionally , pulse arrival time ( PAT ) contains the pre-ejection period ( PEP ) and is prone to smooth muscle ( SM ) contraction . Besides , the implementation of electrocardiogram ( ECG ) requires many measurement sites , which is inconvenient and obtrusive . In this paper , peripheral pulse transit time ( PPTT ) is proposed for NCBP estimation .
It is shown that this technique is better than the existing methods , and the estimates obtained by this technique are more meaningful in evaluating the quality of feature extractors . Computer simulation as well as application to electrocardiogram analysis are used to demonstrate this technique .
The embodiment of the invention provides an electrocardiosignal baseline recognizer which comprises a data input module , an FIFO ( First In First Out ) buffer module , a central control module , a baseline recognition module and a data output module , and the embodiment of the invention further provides an electrocardiosignal baseline recognition method . Through directly recognizing the baseline position , the analytical calculation to the subsequent electrocardiogram analysis is carried out directly based on the baseline position , the baseline drift rejection technology is not required to perform pretreatment , so that the error caused by the application of the baseline drift rejection technology can be effectively reduced , and meanwhile , the subsequent electrocardiogram analysis is simpler and more convenient .
BACKGROUND AND OBJECTIVE Myocardial infarction ( MI ) is one of the most threatening cardiovascular diseases for human beings , which can be diagnosed by electrocardiogram ( ECG ) . Automated detection methods based on ECG focus on extracting handcrafted features .
We present algorithm for Heart Rate detection based on Short-Term Autocorrelation Center Clipping method . This algorithm is dedicated for biological signal detection , electrocardiogram , in noisy environment with lot of artifacts . Using this algorithm is also possible detect the R pointers in the PQRST complex of the ECG signal .
This paper describes a capacitively-coupled chopper instrumentation amplifier for use in electrocardiography ( ECG ) . The amplifier 's gain is accurately defined by a capacitive feedback network , while a DC servo loop rejects the DC offset generated by the electrode-tissue interface .
Ectopic beats are early 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 .
Abstract : The thesis describes the development and results of a real-time computer system for electrocardiographic analysis using the PDP-15 computer . The system was developed using algorithms that were previously developed at the Air Force Institute of Technology and the Cox Heart Institute .
When using the available m-health systems , ECG data for a small duration is recorded and sent to a server for processing and arrhythmia detection . Since arrhythmia occurrence is not so frequent in early stages , a need is felt to develop a real time and continuous arrhythmia monitoring system on the phone itself .
The method has system . A three-channel 24-hour recorded ECG can be completely analyzed within 5 minutes for re orting the heart rate variations , ST level analysis , teart beat classifications and arrhythmia analysis . also been implemented on a P e -486 based computer
Results presented in histogram and cumulative histogram plots showed that the signal quality can be accurately assessed using this new method . Of the total 44 non-paced records , the new method identified 22 records where one of the two leads had much better signal quality than the other lead When this information was used for arrhythmia analysis , the averaged PVC false positive rate was 0.47 % for leads that were selected by the new method , and 2.56 % for leads that were not selected These results clearly showed that the new signal quality measure developed can be used to accurately assess the ECG signal quality and can be incorporated easily into existing arrhythmia algorithms for performance improvement .
Particularly , my special thanks go to JHRS members boasting the highest number of abstracts submitted for each year׳s scientific session ; their papers are also academically invaluable . Amidst the situation where a huge gap still exists between countries in the Asia-Pacific region in terms of research and treatment in the field of arrhythmia , our goal is to help such advanced countries as Japan take a step forward to a globally leading level in this sphere , while assisting undeveloped countries in creating the relevant infrastructure to have the arena of arrhythmia established as an academic field and as a practical care and treatment for patients as well . In order to achieve this goal , I think the academic exchange through “ Journal of Arrhythmia”—the official journal of the JHRS and the APHRS — is significant .
But it is difficult to utilize morphological information and temporal information simultaneously when dealing with single ECG heartbeats , because RR interval is not contained in a single short heartbeat . Therefore , to handle this problems , a novel Multi-information Fusion Convolutional Bidirectional Recurrent Neural Network ( MF-CBRNN ) is proposed for arrhythmia automatic detection . METHODS .
Smartphone or wearable sensor extracted photoplethysmogram ( PPG ) is the sole physiological signal that is considered to characterize the cardiac anomalous events . Our main novelty is to precisely detect and remove the motion artifacts in PPG signals and to ensure accuracy in arrhythmia condition detection , specifically to reduce the false negative alarms . We establish the efficacy of proposed solution , iCarMa by large set of experiments with field-collected and MIT-Physionet PPG signals .
This promises to improve diagnosis accuracy , but the large data volume presents new challenges for cardiologists . This paper introduces ECGLens , an interactive system for arrhythmia detection and analysis using large-scale ECG data . Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network , an outlier detection algorithm , and a set of rich interaction techniques .
In fact , electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues . In this paper , we focus on the arrhythmia detection problem . Specifically , our objective is to address the problem of distributed processing considering big data generated by electrocardiogram ( ECG ) signals in order to carry out pre-processing analysis .
Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential , commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist where noise remains an unsolved problem . Here , we develop a multi-task deep learning method to assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation ( AF ) . We train our algorithm on over one million simulated unlabeled physiological signals and fine-tune on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices .
It is noteworthy that our model can conduct these procedures in a single stream and learn all network parameters jointly in an end-to-end manner . We validated the effectiveness of our method using the public data sets of MIMIC-III and PhysioNet challenge 2012 by comparing with and outperforming other state-of-the-art methods for mortality prediction considered in our experiments . In addition , we identified the behavior of the model that well represented the uncertainties for the imputed estimates , which showed a high correlation between the uncertainties and mean absolute error ( MAE ) scores for imputation .
We also try to conclude , from the two scoring systems presented , which predicts better the situations described . Regarding the results , we concluded that the PULP scoring allows a better mortality prediction achieving , in this case , above 90 % accuracy , however , the results may be inconclusive due to the lack of patients who died in the dataset used . Regarding the complications , we concluded that , on the other hand , the Boey system achieves better results leading to a better prediction when it comes to predicting patients ’ state complication .
However , mortality prediction for ICU patients has typically been conducted by building one common predictive model for all the diseases . In this paper , we incorporate disease-specific contexts into mortality modeling by formulating the mortality prediction problem as a multi-task learning problem in which a task corresponds to a disease . Our method effectively integrates medical domain knowledge relating to the similarity among diseases and the similarity among Electronic Health Records ( EHRs ) into a data-driven approach by incorporating graph Laplacians into the regularization term to encode these similarities .
Using the PAG we develop an algorithm that determines an interventional distribution that is stable to the declared shifts ; this subsumes existing approaches which find stable feature sets that are less accurate . We apply I-SPEC to a mortality prediction problem to show it can learn a model that is robust to shifts without needing upfront knowledge of the full causal DAG .
The goal of this study is to conduct a systematic comparative study of different ML algorithms for several predictive modeling problems in urgent care . Design We assess the performance of 4 benchmark prediction tasks ( eg mortality and prediction , differential diagnostics , and disease marker discovery ) using medical histories , physiological time-series , and demographics data from the Medical Information Mart for Intensive Care ( MIMIC-III ) database . Measurements For each given task , performance was estimated using standard measures including the area under the receiver operating characteristic ( AUC ) curve , F-1 score , sensitivity , and specificity .
Keynote Papers : Analysis of Clustered Multivariate Data from Developmental Toxicity Studies Wavelets in Statistics : Some Recent Developments.- Invited Papers : Mortality Pattern Prediction in Worker Cohorts Design Algorithms for Correlated Data (Co)Variance Structures for Linear Models in the Analysis of Plant Improvement Data Optimal Scaling Methods for Graphical Display of Multivariate Data Computer-assisted Statistics Teaching in Network Environments Modelling Bacterial Genomes using Hidden Markov Models MCMC Specifics for Latent Variable Methods Exploritory versus Decision Trees Exploring Time Series using Semi- and Nonparametric Methods Time Series Forecasting by Principal Component Methods.- Contributed Papers : A Simulation Study of Indirect Prognostic Classification Model Search : an Overview Speeding up the Computation of the Least Quartile Difference Estimator Piece-wise Detection of Spinal Scoliosis A Comparison of Recent EM Accelerators with Item Response Theory Grobner Basis Methods in Polynomial Modelling Analysis of High Dimensional Data from Intensive Care Medicine Fitting Non-Gaussian Time Series Models Using Singular Value Decomposition in Non-Linear Regression A Modelling Approach for Bandwidth Selection in Kernel Density Estimation A New Method for Cross-Classfication Analysis of Contigency Data Tables Bayesian Analysis of Overdispersed Count Data with Application to Teletraffic Monitoring An Alternative Pruning Method Based on the Impurity-Complexity Measure Bayesian Inference for Mixture :
The final classification , considering mortality as the positive class , resulted in an accuracy of 91.56 % , sensitivity of 90.59 % , precision of 86.52 % and F-1-score of 88.50 % . The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose . Moreover , the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs .
Tools for signal filtration , interactive intervals and segment measurements and automatic R wave detection are implemented within the application and are also presented in this paper . The application allows analysis of heart rate variability , a relatively new method for evaluating proper autonomic nervous system ( ANS ) functioning . Analysis of heart rate variability ( HRV ) is based on time-domain methods ( SDNN , RMSSD , pNN50 , HR and mean HR ) and methods of nonlinear dynamics ( ApEn , 0V% , 1V% , 2LV% , 2UV% , Porta index , Guzik index , Ehlers index ) .
Holter recorders are currently changing their typical arrhythmia detection focus towards additional ECG evaluation objectives like ST-T-segment or heart rate variability analysis . Such estimations are only applicable when they are related to normal sinus rhythm excitations .
In this thesis , design and implementation of a new PC-based heart rate variability recording system with respiration is described . The respiration rate is detected by a thermocouple placed in a nasal tube , whereas a single-channel electrocardiogram ( ECG ) signal is recorded for heart rate variability analysis in the system . The PC-based data acquisition part , which was designed in compliance to patient safety standards , has a Universal Serial Bus ( USB ) interface .
Contact measurements of the cardiac pulse by using whether conventional electrocardiogram equipment or commercially-available pulse oximetry sensors would cause inconvenience for patients . Therefore , a novel robust non-contact technique is developed for the evaluation of heart rate variation . Image-based heart rate measurement based on multiple regression method is proposed .
Commercially available pulse oximetry sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can be painful to use . Therefore , a novel robust non-contact technique is developed for the evaluation of heart rate variation . According to the periodic variation of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as heartbeats cause changes to blood volume in the blood vessels in the face , a reflectance signal is decomposed from consecutive frames of the green channel of the facial region .
HRV ( Heart Rate Variability ) monitoring is a relatively new method for measuring the effects of stress on the human body . Through monitoring HRV data , doctors can quantify the health status of the patient .
The device was evaluated under three different scenarios , each one designed to test the different ExG modalities . Initial tests showed promising results and allowed for ( 1 ) steady-state visually evoked potentials to be accurately measured from EEG , ( 2 ) heart rate variability measurements to discriminate between different affective videos , and ( 3 ) EOG measurements to monitor gaze direction and eye blinks , all while users were mobile . Being able to accurately monitor signals from the autonomic and central nervous systems in an unobtrusive and portable manner is an important step for instrumental QoE assessment of emerging VR/AR applications .
Non-contact heart rate and heart rate variability measurements have applications in healthcare and affective computing . Recently , a system utilizing a five-band camera ( RGBCO : red , green , blue , cyan , orange ) was proposed , and shown to improve both remote measurement of heart rate and heart rate variability over an RGB camera .
The current paper proposes an application of methods from visual analytics to time-frequency energy distribution in ECG signals , with the goal of studying heart rate variability from the main ECG band corresponding to heart rate . Such an approach can complement standard time- and frequency-domain techniques for heart rate variability assessment . Multiresolution wavelet entropy was computed from continuous wavelet transforms using windows of user-specified length and overlap .
This thesis suggests that nanotechnological devices may serve as a potential alternative to other heart rate measuring technology . Limitations in detecting minor heart rate changes between different driving conditions and in heart rate variability measuring were also acknowledged .
Heart rate variability and motion artifact are two major obstacles to the photoplethysmographic ( PPG ) signal analysis , which is a very promising tool to derive useful information about the hemodynamics as well as autonomic nerve system . This paper suggests employing a truncation/extrapolation method on the PPG signal or its first order derivative to overcome the heart rate variability problem while extract a refined mean PPG pulse waveform . Meanwhile , the cross-correlation detection method is employed to remove the motion artifacts .
The forward problem of electrocardiography: from heart models to body surface potentials Enhancing interference rejection of preamplified electrodes by automated gain adaption Noninvasive continuous blood pressure estimation with peripheral pulse transit time Nonparametric Estimation of the Bayes Error of Feature Extractors Using Ordered Nearest Neighbor Sets Device and method for electrocardiosignal baseline recognition ML-ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG Robust Algorithm for Heart Rate (HR) Detection and Heart Rate Variability (HRV) Estimation A 2.1 μW area-efficient capacitively-coupled chopper instrumentation amplifier for ECG applications in 65 nm CMOS Ventricular activity morphological characterization: Ectopic beats removal in long term atrial fibrillation recordings The Development of a Real-Time Electrocardiogram Analyzing System Using the PDP-15 Computer. m-QRS: An efficient QRS detection algorithm for mobile health applications A Technique for Au Detection of A new method for evaluating ECG signal quality for multi-lead arrhythmia analysis Comments from the President of APHRS Multi-information fusion neural networks for arrhythmia automatic detection iCarMa: Inexpensive Cardiac Arrhythmia Management -- An IoT Healthcare Analytics Solution ECGLens: Interactive Visual Exploration of Large Scale ECG Data for Arrhythmia Detection A Big Data Analytics Approach for the Development of Advanced Cardiology Applications DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction Predicting Death and Morbidity in Perforated Peptic Ulcer Simultaneous Modeling of Multiple Diseases for Mortality Prediction in Acute Hospital Care I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models Predictive modeling in urgent care: a comparative study of machine learning approaches COMPSTAT 1998, Proceedings in Computational Statistics 13th Symposium held in Bristol, Great Britain, 1998 Feature engineering for ICU mortality prediction based on hourly to bi-hourly measurements Software Tool for Processing and Analysis of ECG Signal Wave sequence based identification of sinus rhythm beats on a microcontroller Design and implementation of a PC-based heart rate variability and respiration recording system Image-based heart rate measurement based on multiple regression Face-Based Heart Rate Signal Decomposition and Evaluation Using Multiple Linear Regression iHRV: Cloud-Based Mobile Heart Rate Variability Monitoring System A Neurophysiological Sensor-Equipped Head-Mounted Display for Instrumental QoE Assessment of Immersive Multimedia Improvements in remote video based estimation of heart rate variability using the Welch FFT method Analyzing multiresolution wavelet entropy of ECG with visual analytics techniques ASSESSING THE EFFECTS OF COGNITIVE SECONDARY TASKS AND AUTOMATION TYPE ON CHANGES IN HEART RATE: IMPLICATIONS FOR THE POTENTIAL USE OF NANOTECHNOLOGY An Improved Pre-processing Approach for Photoplethysmographic Signal
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