Open AccessEfficient dynamic modelling of ECG with myocardial infarction using interacting multiple model and particle filter
Author(s) -
Swain Sushree Satvatee,
Patra Dipti
Publication year - 2020
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2019.0458
Subject(s) - computer science , signal (programming language) , artificial intelligence , particle filter , filter (signal processing) , pattern recognition (psychology) , kalman filter , noise (video) , identification (biology) , speech recognition , computer vision , botany , image (mathematics) , biology , programming language
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.