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
研究基于Van der Pol方程的心律模型有助于进行心律调控,为心律失常等心脏疾病治疗提供指导。针对已有Van der Pol方程时间尺度与真实生物心律不一致的问题,引入时间尺度参数,改进Van der Pol方程。针对心律模型的数值模拟问题,提出一...研究基于Van der Pol方程的心律模型有助于进行心律调控,为心律失常等心脏疾病治疗提供指导。针对已有Van der Pol方程时间尺度与真实生物心律不一致的问题,引入时间尺度参数,改进Van der Pol方程。针对心律模型的数值模拟问题,提出一种基于深度神经网络的算法。针对现有神经网络算法带来的假解问题,提出一种新的采样策略构建训练数据集。为进一步提高求解的精度,引入自适应采样策略。开展数值实验,将Runge-Kutta法给出的数值解与所提深度神经网络算法的计算结果进行对比。结果显示,深度神经网络算法的计算结果相对于Runge-Kutta数值解的最大平均误差不超过0.3%。还利用深度神经网络算法模拟牛蛙心脏搏动细胞动作电位信号以及外部驱动信号对该电位信号的调控作用,结果显示,深度神经网络算法可以很好地模拟动作电位的脉冲波形及外部驱动信号的调控作用。展开更多
A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A g...A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A group of healthy persons are studied and the results indicate that this method can effectively get nonlinear information from short (6—7 min) heartbeat series and consequently reflect the degree of heart rate variability, which supplies convenience in clinical application. Finally, a comparison with the traditional time domain method shows that the NAR model method can reflect the complexity of the whole signal and lessen the influence of noise and instability, in the signal.展开更多
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.
文摘研究基于Van der Pol方程的心律模型有助于进行心律调控,为心律失常等心脏疾病治疗提供指导。针对已有Van der Pol方程时间尺度与真实生物心律不一致的问题,引入时间尺度参数,改进Van der Pol方程。针对心律模型的数值模拟问题,提出一种基于深度神经网络的算法。针对现有神经网络算法带来的假解问题,提出一种新的采样策略构建训练数据集。为进一步提高求解的精度,引入自适应采样策略。开展数值实验,将Runge-Kutta法给出的数值解与所提深度神经网络算法的计算结果进行对比。结果显示,深度神经网络算法的计算结果相对于Runge-Kutta数值解的最大平均误差不超过0.3%。还利用深度神经网络算法模拟牛蛙心脏搏动细胞动作电位信号以及外部驱动信号对该电位信号的调控作用,结果显示,深度神经网络算法可以很好地模拟动作电位的脉冲波形及外部驱动信号的调控作用。
文摘A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A group of healthy persons are studied and the results indicate that this method can effectively get nonlinear information from short (6—7 min) heartbeat series and consequently reflect the degree of heart rate variability, which supplies convenience in clinical application. Finally, a comparison with the traditional time domain method shows that the NAR model method can reflect the complexity of the whole signal and lessen the influence of noise and instability, in the signal.
