Jafarnia-Dabanloo N, McLernon D C, Zhang H, Ayatollahi A, Johari-Majd V
School of Electronic and Electrical Engineering, The University of Leeds, Leeds, UK.
J Theor Biol. 2007 Jan 21;244(2):180-9. doi: 10.1016/j.jtbi.2006.08.005. Epub 2006 Aug 12.
Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)--all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low-frequency component in the power spectrum of HRV. We use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the model's ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions. As such the model provides a useful tool to simulate and analyse the main characteristics of ECG, such as its power spectrum and HRV under different conditions. Potential applications of this model include using the generated ECG as a flexible signal source to assess the effectiveness of a diagnostic ECG signal-processing device.
开发用于人工生成心电图（ECG）信号的数学模型是一个已被广泛研究的课题。其中一个挑战是生成具有广泛波形、功率谱和心率变异性（HRV）变化的ECG信号，所有这些都是人类心脏功能的重要指标。在本文中，我们提出了一个用于生成此类人工ECG信号的综合模型。我们将呼吸性窦性心律失常、迈尔波以及HRV功率谱中重要的极低频成分的影响纳入我们的模型。我们使用一种新的改进的泽曼模型来生成HRV的时间序列，并通过一个简单的神经网络生成单个ECG周期。这项工作的重要性在于该模型能够生成在各种生理条件下类似于实验记录的人工ECG信号。因此，该模型提供了一个有用的工具来模拟和分析ECG的主要特征，例如其在不同条件下的功率谱和HRV。该模型的潜在应用包括将生成的ECG用作灵活的信号源，以评估诊断ECG信号处理设备的有效性。
