1 Introduction According to the World Health Organization,heart disease has been the leading cause of death worldwide for the past 20 years.Electrocardiography(ECG or EKG)records the electrophysiological activity of t...1 Introduction According to the World Health Organization,heart disease has been the leading cause of death worldwide for the past 20 years.Electrocardiography(ECG or EKG)records the electrophysiological activity of the heart in time,allowing accurate diagnoses by clinicians[1].Despite the relative simplicity of ECG acquisition,its interpretation requires extensive training.Manual examination and re-examination of ECG paper records can be time-consuming,potentially delaying diagnosis.Machine learning,which uses algorithms to identify patterns within data and make predictive analyses,has played a significant role in interpreting ECGs[2].展开更多
Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences betwee...Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences between good and poor heartbeat perceivers at attention and resting state.Methods Thirty channels of electroencephalogram（EEG）were recorded in 22 subjects,who had been subdivided into good and poor heartbeat perceivers by mental tracking task. Principal component analysis（PCA）was applied to remove cardiac field artifact（CFA）from the HEP.Results（1）The good heart-beat perceivers showed difference between attention and resting state in the windows from 250 ms to 450 ms after R wave at C3 location and from 100 ms to 300 ms after R wave at C4 location;（2）The difference waveforms between good and poor heartbeat perceivers was a positive waveform at FZ from 220 ms to 340 ms after R wave,which was more significant in attention state.Conclusion Attention state had more effect on the HEPs of good heartbeat perceivers than that of poor heartbeat perceivers;and perception ability influenced HEPs more strongly in the attention state than in the resting state.展开更多
The analysis and design of observed-based nonlinear control of a heartbeat tracking system is investigated in this paper. Two of Zeeman’s heartbeat models are investigated and modified by adding the control input as ...The analysis and design of observed-based nonlinear control of a heartbeat tracking system is investigated in this paper. Two of Zeeman’s heartbeat models are investigated and modified by adding the control input as a pacemaker, thereby creating the control-affine nonlinear system models that capture the general heartbeat behavior of the human heart. The control objective is to force the output of the heartbeat models to track and generate a synthetic electrocardiogram (ECG) signal based on the actual patient reference data, obtained from the William Beaumont Hospitals, Michigan, and the PhysioNet database. The formulations of the proposed heartbeat tracking control systems consist of two phases: analysis and synthesis. In the analysis phase, nonlinear controls based on input-output feedback linearization are considered. This approach simplifies the difficult task of developing nonlinear controls. In the synthesis phase, observer-based controls are employed, where the unmeasured state variables are estimated for practical implementations. These observer-based nonlinear feedback control schemes may be used as a control strategy in electronic pacemakers. In addition, they could be used in a software-based approach to generate a synthetic ECG signal to assess the effectiveness of diagnostic ECG signal processing devices.展开更多
In cloud computing environment, as the infrastructure not owned by users, it is desirable that its security and integrity must be protected and verified time to time. In Hadoop based scalable computing setup, malfunct...In cloud computing environment, as the infrastructure not owned by users, it is desirable that its security and integrity must be protected and verified time to time. In Hadoop based scalable computing setup, malfunctioning nodes generate wrong output during the run time. To detect such nodes, we create collaborative network between worker node (i.e. data node of Hadoop) and Master node (i.e. name node of Hadoop) with the help of trusted heartbeat framework (THF). We propose procedures to register node and to alter status of node based on reputation provided by other co-worker nodes.展开更多
Extraction of foetal heartbeat rate from a single passive sound sensor on the mother’s abdomen is demonstrated. The extraction is based on the assumption that a disjoint band of frequencies exist and foetal signal is...Extraction of foetal heartbeat rate from a single passive sound sensor on the mother’s abdomen is demonstrated. The extraction is based on the assumption that a disjoint band of frequencies exist and foetal signal is concentrated in this band, and further that it can be represented conveniently as a set of wavelet coefficients. The algorithm has been applied to each stream of data obtained from six different channels and the detection performance is elaborated. The algorithm has also been tested on signals from non-pregnant abdomens to show successful rejection of adult heartbeat. The extraction of the desired signal is done in two stages so as to eliminate components from the maternal heart-beat.展开更多
The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of parti...The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of particle swarm optimization(PSO)hybridized with the active-set algorithm(ASA),i.e.,ANNs-PSO-ASA.The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study.An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions.The optimization of the merit function is accomplished using the hybrid computing performances of PSO-ASA.The designed performance of ANNs-PSO-ASA is implemented for the numerical treatment of the VP-HBM dynamics by fluctuating the pulse shape adjustment terms,external forcing factor and damping coefficient with fixed ventricular contraction period.To perform the correctness of the present scheme,the obtained numerical results through the designed ANN-PSO-ASA will be compared with the Adams numerical method.The statistical investigations with larger dataset are provided using the“mean absolute deviation”,“Theil’s inequality coefficient”and“variance account for”operators to perform the applicability,reliability,and effectiveness of the designed ANNs-PSO-ASA scheme for solving the VP-HBM.展开更多
In most of fault detection algorithms of distributed system, fault model is restricted to fault of process, and link failure is simply masked, or modeled by process failure. Both methods can soon use up system resourc...In most of fault detection algorithms of distributed system, fault model is restricted to fault of process, and link failure is simply masked, or modeled by process failure. Both methods can soon use up system resource and potentially reduce the availability of system. A fault Detection Protocol based on Heartbeat of multiple Master-nodes (DPHM) is proposed, which can immediately and accurately detect and locate faulty links by adopting voting and electing mechanism among master-nodes. Thus, DPHM can effectively improve availability of system. In addition, in contrast with other detection protocols, DPHM reduces greatly the detection cost due to the structure of master-nodes.展开更多
Cardiovascular and cerebrovascular events have been observed during night-time associated with periodic breathing including sleep apnea and Cheyne-Stokes respiration. Early detection and treatment is important to redu...Cardiovascular and cerebrovascular events have been observed during night-time associated with periodic breathing including sleep apnea and Cheyne-Stokes respiration. Early detection and treatment is important to reduce night-time events. We clarified the characteristics of the dynamic nature of heartbeats associated with periodic breathing by using detrended fluctuation analysis (DFA). We analyzed heartbeats in eight recordings from the MIT-BIH Polysomnographic database. We observed two crossover points and defined three scaling exponents, β1 (n ≤ 40 beats), β2 (50 ≤ n ≤ 200), and β3 (251 ≤ n ≤ 1584). Compared with β1 (1.21 ± 0.13) and β3 (0.92 ± 0.16), scaling exponent β2 (0.62 ± 0.16) showed the statistically lowest value (p 0.05). And there was a negative relationship between the scaling exponent β2 and apnea/hypopnea index (p 0.05). These results indicate that DFA analysis of heartbeats may be useful for the early detection of sleep associated breathing disorders including sleep apnea and its severity.展开更多
Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accura...Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model＇ s coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous （ARX） model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.展开更多
Owing to the recent trends in remote health monitoring,real-time appli-cations for measuring Heartbeat Rate and Respiration Rate(HARR)from video signals are growing rapidly.Photo Plethysmo Graphy(PPG)is a method that ...Owing to the recent trends in remote health monitoring,real-time appli-cations for measuring Heartbeat Rate and Respiration Rate(HARR)from video signals are growing rapidly.Photo Plethysmo Graphy(PPG)is a method that is operated by estimating the infinitesimal change in color of the human face,rigid motion of facial skin and head parts,etc.Ballisto Cardiography(BCG)is a non-surgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses.The resilience against motion artifacts induced by luminancefluctuation and the patient’s mobility var-iation is the major difficulty faced while processing the real-time video signals.In this research,a video-based HARR measuring framework is proposed based on combined PPG and BCG.Here,the noise from the input video signals is removed by using an Adaptive Kalmanfilter(AKF).Three different algorithms are used for estimating the HARR from the noise-free input signals.Initially,the noise-free sig-nals are subjected to Modified Adaptive Fourier Decomposition(MAFD)and then to Enhanced Hilbert vibration Decomposition(EHVD)andfinally to Improved Var-iation mode Decomposition(IVMD)for attaining three various results of HARR.The obtained values are compared with each other and found that the EHVD is showing better results when compared with all the other methods.展开更多
The blade servers have been widely used in the telecommunications,financial and other big data processing fields as for the high efficiency, stability and autonomy. This study takes the hot redundancy design for dual-...The blade servers have been widely used in the telecommunications,financial and other big data processing fields as for the high efficiency, stability and autonomy. This study takes the hot redundancy design for dual-BMC management of blade servers as the research project, and puts forward a heartbeat detection program utilizing I2C for transmission of IPMI commands. And it’s successfully applied to the blade servers to achieve a hot redundancy, monitor and management of master/slave BMC management module, which is more standardized, reliable, and easy to implement.展开更多
Electrocardiogram(ECG)is widely used to detect arrhythmia.Atrial fibrillation,atrioventricular block,premature beats,etc.can all be diagnosed by ECG.When the distribution of training data and test data is inconsistent...Electrocardiogram(ECG)is widely used to detect arrhythmia.Atrial fibrillation,atrioventricular block,premature beats,etc.can all be diagnosed by ECG.When the distribution of training data and test data is inconsistent,the accuracy of the model will be affected.This phenomenon is called dataset shift.In the real-world heartbeat classification system,the heartbeat of the training set and test set often comes from patients of different ages and genders,so there are differences in the distribution of data sets.The main challenge in applying machine learning algorithms to clinical AI systems is dataset shift.Test-time adaptation(TTA)aims to adapt a pre-trained model from the source domain(SD)to the target domain(TD)without using any SD data or TD labels,thereby reducing model performance degradation due to domain differences.We propose a method based on multimodal image fusion and continual test-time adaptation(FCTA)for accurate and efficient heartbeat classification.First,the original ECG data is converted into a three-channel color image through a multimodal image fusion framework.The impact of class imbalance on network performance is overcome using a batch weight loss function,and then the pretrained source model is adapted to the TD using a continual test-time adaptation(CTA)method.Although our method is very simple,compared with other domain adaptation methods,it can significantly improve model performance on the test set and reduce the impact caused by the difference in domain distribution.展开更多
基金supported by the NSFC-FDCT Grant 62361166662the National Key R&D Program of China(2023YFC3503400,2022YFC3400400)+4 种基金the Innovative Research Group Project of Hunan Province(2024JJ1002)the Key R&D Program of Hunan Province(2023GK2004,2023SK2059,2023SK2060)the Top 10 Technical Key Project in Hunan Province(2023GK1010)the Key Technologies R&D Program of Guangdong Province(2023B1111030004 to FFH)the Funds of the National Supercomputing Center in Changsha.
文摘1 Introduction According to the World Health Organization,heart disease has been the leading cause of death worldwide for the past 20 years.Electrocardiography(ECG or EKG)records the electrophysiological activity of the heart in time,allowing accurate diagnoses by clinicians[1].Despite the relative simplicity of ECG acquisition,its interpretation requires extensive training.Manual examination and re-examination of ECG paper records can be time-consuming,potentially delaying diagnosis.Machine learning,which uses algorithms to identify patterns within data and make predictive analyses,has played a significant role in interpreting ECGs[2].
基金the National Natural Science Foundation of China（No.30400105）；the National Basic Research Development Program（973）（No. 2003CB716106）；the National Science Fund for Distinguished Young Scholars of China（No.30525030）.
文摘Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences between good and poor heartbeat perceivers at attention and resting state.Methods Thirty channels of electroencephalogram（EEG）were recorded in 22 subjects,who had been subdivided into good and poor heartbeat perceivers by mental tracking task. Principal component analysis（PCA）was applied to remove cardiac field artifact（CFA）from the HEP.Results（1）The good heart-beat perceivers showed difference between attention and resting state in the windows from 250 ms to 450 ms after R wave at C3 location and from 100 ms to 300 ms after R wave at C4 location;（2）The difference waveforms between good and poor heartbeat perceivers was a positive waveform at FZ from 220 ms to 340 ms after R wave,which was more significant in attention state.Conclusion Attention state had more effect on the HEPs of good heartbeat perceivers than that of poor heartbeat perceivers;and perception ability influenced HEPs more strongly in the attention state than in the resting state.
文摘The analysis and design of observed-based nonlinear control of a heartbeat tracking system is investigated in this paper. Two of Zeeman’s heartbeat models are investigated and modified by adding the control input as a pacemaker, thereby creating the control-affine nonlinear system models that capture the general heartbeat behavior of the human heart. The control objective is to force the output of the heartbeat models to track and generate a synthetic electrocardiogram (ECG) signal based on the actual patient reference data, obtained from the William Beaumont Hospitals, Michigan, and the PhysioNet database. The formulations of the proposed heartbeat tracking control systems consist of two phases: analysis and synthesis. In the analysis phase, nonlinear controls based on input-output feedback linearization are considered. This approach simplifies the difficult task of developing nonlinear controls. In the synthesis phase, observer-based controls are employed, where the unmeasured state variables are estimated for practical implementations. These observer-based nonlinear feedback control schemes may be used as a control strategy in electronic pacemakers. In addition, they could be used in a software-based approach to generate a synthetic ECG signal to assess the effectiveness of diagnostic ECG signal processing devices.
文摘In cloud computing environment, as the infrastructure not owned by users, it is desirable that its security and integrity must be protected and verified time to time. In Hadoop based scalable computing setup, malfunctioning nodes generate wrong output during the run time. To detect such nodes, we create collaborative network between worker node (i.e. data node of Hadoop) and Master node (i.e. name node of Hadoop) with the help of trusted heartbeat framework (THF). We propose procedures to register node and to alter status of node based on reputation provided by other co-worker nodes.
文摘Extraction of foetal heartbeat rate from a single passive sound sensor on the mother’s abdomen is demonstrated. The extraction is based on the assumption that a disjoint band of frequencies exist and foetal signal is concentrated in this band, and further that it can be represented conveniently as a set of wavelet coefficients. The algorithm has been applied to each stream of data obtained from six different channels and the detection performance is elaborated. The algorithm has also been tested on signals from non-pregnant abdomens to show successful rejection of adult heartbeat. The extraction of the desired signal is done in two stages so as to eliminate components from the maternal heart-beat.
基金This research received funding support from the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation(Grant Number B05F640088).
文摘The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of particle swarm optimization(PSO)hybridized with the active-set algorithm(ASA),i.e.,ANNs-PSO-ASA.The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study.An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions.The optimization of the merit function is accomplished using the hybrid computing performances of PSO-ASA.The designed performance of ANNs-PSO-ASA is implemented for the numerical treatment of the VP-HBM dynamics by fluctuating the pulse shape adjustment terms,external forcing factor and damping coefficient with fixed ventricular contraction period.To perform the correctness of the present scheme,the obtained numerical results through the designed ANN-PSO-ASA will be compared with the Adams numerical method.The statistical investigations with larger dataset are provided using the“mean absolute deviation”,“Theil’s inequality coefficient”and“variance account for”operators to perform the applicability,reliability,and effectiveness of the designed ANNs-PSO-ASA scheme for solving the VP-HBM.
基金the National Natural Science Foundation of China (No.60503015).
文摘In most of fault detection algorithms of distributed system, fault model is restricted to fault of process, and link failure is simply masked, or modeled by process failure. Both methods can soon use up system resource and potentially reduce the availability of system. A fault Detection Protocol based on Heartbeat of multiple Master-nodes (DPHM) is proposed, which can immediately and accurately detect and locate faulty links by adopting voting and electing mechanism among master-nodes. Thus, DPHM can effectively improve availability of system. In addition, in contrast with other detection protocols, DPHM reduces greatly the detection cost due to the structure of master-nodes.
文摘Cardiovascular and cerebrovascular events have been observed during night-time associated with periodic breathing including sleep apnea and Cheyne-Stokes respiration. Early detection and treatment is important to reduce night-time events. We clarified the characteristics of the dynamic nature of heartbeats associated with periodic breathing by using detrended fluctuation analysis (DFA). We analyzed heartbeats in eight recordings from the MIT-BIH Polysomnographic database. We observed two crossover points and defined three scaling exponents, β1 (n ≤ 40 beats), β2 (50 ≤ n ≤ 200), and β3 (251 ≤ n ≤ 1584). Compared with β1 (1.21 ± 0.13) and β3 (0.92 ± 0.16), scaling exponent β2 (0.62 ± 0.16) showed the statistically lowest value (p 0.05). And there was a negative relationship between the scaling exponent β2 and apnea/hypopnea index (p 0.05). These results indicate that DFA analysis of heartbeats may be useful for the early detection of sleep associated breathing disorders including sleep apnea and its severity.
基金the National Basic Research Program of China(No.2003CB314806)China Next Generation Intemet Project(CNGI-04-6-2T)
文摘Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model＇ s coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous （ARX） model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.
文摘Owing to the recent trends in remote health monitoring,real-time appli-cations for measuring Heartbeat Rate and Respiration Rate(HARR)from video signals are growing rapidly.Photo Plethysmo Graphy(PPG)is a method that is operated by estimating the infinitesimal change in color of the human face,rigid motion of facial skin and head parts,etc.Ballisto Cardiography(BCG)is a non-surgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses.The resilience against motion artifacts induced by luminancefluctuation and the patient’s mobility var-iation is the major difficulty faced while processing the real-time video signals.In this research,a video-based HARR measuring framework is proposed based on combined PPG and BCG.Here,the noise from the input video signals is removed by using an Adaptive Kalmanfilter(AKF).Three different algorithms are used for estimating the HARR from the noise-free input signals.Initially,the noise-free sig-nals are subjected to Modified Adaptive Fourier Decomposition(MAFD)and then to Enhanced Hilbert vibration Decomposition(EHVD)andfinally to Improved Var-iation mode Decomposition(IVMD)for attaining three various results of HARR.The obtained values are compared with each other and found that the EHVD is showing better results when compared with all the other methods.
文摘The blade servers have been widely used in the telecommunications,financial and other big data processing fields as for the high efficiency, stability and autonomy. This study takes the hot redundancy design for dual-BMC management of blade servers as the research project, and puts forward a heartbeat detection program utilizing I2C for transmission of IPMI commands. And it’s successfully applied to the blade servers to achieve a hot redundancy, monitor and management of master/slave BMC management module, which is more standardized, reliable, and easy to implement.
文摘Electrocardiogram(ECG)is widely used to detect arrhythmia.Atrial fibrillation,atrioventricular block,premature beats,etc.can all be diagnosed by ECG.When the distribution of training data and test data is inconsistent,the accuracy of the model will be affected.This phenomenon is called dataset shift.In the real-world heartbeat classification system,the heartbeat of the training set and test set often comes from patients of different ages and genders,so there are differences in the distribution of data sets.The main challenge in applying machine learning algorithms to clinical AI systems is dataset shift.Test-time adaptation(TTA)aims to adapt a pre-trained model from the source domain(SD)to the target domain(TD)without using any SD data or TD labels,thereby reducing model performance degradation due to domain differences.We propose a method based on multimodal image fusion and continual test-time adaptation(FCTA)for accurate and efficient heartbeat classification.First,the original ECG data is converted into a three-channel color image through a multimodal image fusion framework.The impact of class imbalance on network performance is overcome using a batch weight loss function,and then the pretrained source model is adapted to the TD using a continual test-time adaptation(CTA)method.Although our method is very simple,compared with other domain adaptation methods,it can significantly improve model performance on the test set and reduce the impact caused by the difference in domain distribution.
