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
The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract ...The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them.Thus,a computerized system is needed to classify ECG signals with more accurate results effectively.Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths.In this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG signals.It consists of four stages;preprocessing,feature extraction,feature optimization and classifier.At first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing stage.It uses a recursive filter to eliminate muscle noise,T-wave interference and baseline wander.As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction stage.Then,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization stage.Finally,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five classes.In SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD system.Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis.It correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet coefficients.The CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features with the RBF classifier.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 theorems for testing the local in one-port cellular neural/inear network (CNN) cells with four local state variables are presented. Using the theorems computes the bifurcation diagrams of the cardiac Purkinje f...The theorems for testing the local in one-port cellular neural/inear network (CNN) cells with four local state variables are presented. Using the theorems computes the bifurcation diagrams of the cardiac Purkinje fiber (CPE) equations Which describethe long-lasting action and pace-maker potentials of the Purkinje fiber of the heart. The computer simulation shows that periodic trajectories or convergent trajectories of the CPF Equations can be foun if the cormsponing cell Parameters are located in a positive domainbut nearby edge of chaos. In particular a heart with approximate normal frequency of heartbeat but non-normal electrocardiogram maysuddenly stop by slightly perturbing the parameters of the corresponding CPF Equations when the Paramders are located nearby the edge of chaos in the bifurcation diagrams. This research seems to interpret reasonbly the phenomena that patients with cardiac diseases might suddenly die without warning.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
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.
文摘The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them.Thus,a computerized system is needed to classify ECG signals with more accurate results effectively.Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths.In this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG signals.It consists of four stages;preprocessing,feature extraction,feature optimization and classifier.At first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing stage.It uses a recursive filter to eliminate muscle noise,T-wave interference and baseline wander.As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction stage.Then,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization stage.Finally,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five classes.In SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD system.Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis.It correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet coefficients.The CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features with the RBF classifier.
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 theorems for testing the local in one-port cellular neural/inear network (CNN) cells with four local state variables are presented. Using the theorems computes the bifurcation diagrams of the cardiac Purkinje fiber (CPE) equations Which describethe long-lasting action and pace-maker potentials of the Purkinje fiber of the heart. The computer simulation shows that periodic trajectories or convergent trajectories of the CPF Equations can be foun if the cormsponing cell Parameters are located in a positive domainbut nearby edge of chaos. In particular a heart with approximate normal frequency of heartbeat but non-normal electrocardiogram maysuddenly stop by slightly perturbing the parameters of the corresponding CPF Equations when the Paramders are located nearby the edge of chaos in the bifurcation diagrams. This research seems to interpret reasonbly the phenomena that patients with cardiac diseases might suddenly die without warning.
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.
