The automatic identification of cardiac condition through dynamic modelling of electrocardiogram (ECG) signal has immense clinical significance as it eliminates the task of manual annotation of ECG recordings. In this study, an interacting multiple model (IMM)-based scheme has been proposed that helps in dynamically modelling and estimating the ECG signal. This model is having the adaptability of interchanging among several morphological representations. It offers the advantage of not necessitating user-specific parameters. This model does not necessitate a priori information about the ECG signal to initialise the filter parameters and delimitation of fiducial points of ECG signal. The particle filter (PF)-based schemes show superiority owing to their freedom from a single assumption on the signal model and noise model. Besides, it has got the potential of simultaneously tracking multiple pathological and morphological changes occurring in biomedical signals. Thus, the parameters of the model are estimated by adopting the PF, so that the myocardial infarction (MI) affected ECG signals can be efficiently tracked. Investigations on ECG signals from the MIT-BIH database and PTB diagnostic ECG database signify that the IMM-PF scheme can represent several MI morphologies with minimum prior information without distorting the helpful diagnostic information of ECG accurately.

中文翻译：

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使用多模型和粒子过滤器交互的高效心电图对心肌梗死的动态建模

通过动态建模心电图（ECG）信号自动识别心脏状况具有巨大的临床意义，因为它消除了手动注释ECG记录的任务。在这项研究中，提出了一种基于交互多模型（IMM）的方案，该方案有助于动态建模和估计ECG信号。该模型具有在几种形态学表示之间互换的适应性。它具有不需要特定于用户的参数的优点。该模型不需要有关ECG信号的先验信息即可初始化滤波器参数和ECG信号基准点的定界。基于粒子滤波器（PF）的方案之所以具有优势，是因为它们摆脱了信号模型和噪声模型的单一假设。除了，它具有同时追踪生物医学信号中发生的多种病理和形态变化的潜力。因此，通过采用PF可以估算模型的参数，从而可以有效地跟踪受心肌梗塞（MI）影响的ECG信号。对来自MIT-BIH数据库和PTB诊断ECG数据库的ECG信号的研究表明，IMM-PF方案可以用最少的先验信息表示几种MI形态，而不会准确地扭曲有用的ECG诊断信息。