Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
Physiol Meas. 2010 Oct;31(10):1309-29. doi: 10.1088/0967-3334/31/10/002. Epub 2010 Aug 18.
In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of electrocardiogram (ECG) signals. It is shown that this model may be effectively used for generating synthetic ECGs as well as separate characteristic waves (CWs) such as the atrial and ventricular complexes. The model uses separate state variables for each CW, i.e. P, QRS and T, and hence is capable of generating individual synthetic CWs as well as realistic ECG signals. The model is therefore useful for generating arrhythmias. Simulations of sinus bradycardia, sinus tachycardia, ventricular flutter, atrial fibrillation and ventricular tachycardia are presented. In addition, discrete versions of the equations are presented for a model-based Bayesian framework for denoising. This framework, together with an extended Kalman filter and extended Kalman smoother, was used for denoising the ECG for both normal rhythms and arrhythmias. For evaluating the denoising performance, the signal-to-noise ratio (SNR) improvement of the filter outputs and clinical parameter stability were studied. The results demonstrate superiority over a wide range of input SNRs, achieving a maximum 12.7 dB improvement. Results indicate that preventing clinically relevant distortion of the ECG is sensitive to the number of model parameters. Models are presented which do not exhibit such distortions. The approach presented in this paper may therefore serve as an effective framework for synthetic ECG generation and model-based filtering of noisy ECG recordings.
在本文中，我们描述了一种基于高斯波的状态空间模型，用于模拟心电图（ECG）信号的时间动态。结果表明，该模型可有效地用于生成合成 ECG 以及分离的特征波（CW），如心房和心室复合波。该模型为每个 CW 使用单独的状态变量，即 P、QRS 和 T，因此能够生成单个合成 CW 和现实的 ECG 信号。因此，该模型可用于生成心律失常。呈现了窦性心动过缓、窦性心动过速、心室扑动、心房颤动和室性心动过速的模拟。此外，还呈现了用于基于模型的贝叶斯去噪的离散方程版本。该框架与扩展卡尔曼滤波器和扩展卡尔曼平滑器一起，用于对正常节律和心律失常的 ECG 进行去噪。为了评估去噪性能，研究了滤波器输出的信噪比（SNR）改善和临床参数稳定性。结果表明，在广泛的输入 SNR 范围内具有优越性，最大可实现 12.7 dB 的改善。结果表明，防止 ECG 的临床相关失真对模型参数的数量敏感。呈现了不会出现这种失真的模型。因此，本文提出的方法可以作为合成 ECG 生成和基于模型的噪声 ECG 记录滤波的有效框架。
