Classification of cardiac electrical signals between patients with myocardial infarction and normal subjects by using nonlinear dynamics features and different classification models. (January 2023)

• Record Type: Journal Article
• Authors: Deng, Muqing; Huang, Xiaoyu; Liang, Zhigao; Lin, Wentao; Mo, Beixi; Liang, Dakai; Ruan, Shuhua; Chen, Jie
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Myocardial infarction (MI) detection -- Electrocardiogram (ECG) -- Vectorcardiogram (VCG) -- Cardiac dynamics -- Feature selection; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104105
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Myocardial infarction (MI) is one of the leading causes of human mortality and morbidity around the world. Despite that much progress has been made for MI detection based on medical image analysis in recent years, most of them suffer from their expensiveness and invasive nature. In this paper, we demonstrate abnormalities of electrocardiogram (ECG) in a new quantifiable manner using nonlinear dynamics features and different classification models. Time-varying ECG data are represented as three-dimensional vectorcardiogram (VCG) and the underlying cardiac dynamics. Nonlinear dynamics measures, including entropy variability measures, complexity measures and chaotic measures, are calculated and fed into different machine learning methods for the classification task. The extracted nonlinear dynamics measures reflect in-depth cardiac dynamics characteristic, which is shown to be more sensitive to subtle ECG modifications. Therefore, it is expected to provide an early detection tool for latent ECG modifications before obvious diagnostic changes are observed. Experiments on the PTB database are conducted to demonstrate the efficiency of the proposed method. Highlights: We demonstrate abnormalities of ECG in a new quantifiable manner. Vectorcardiogram and the underlying cardiac dynamics are extracted. Nonlinear dynamics measures are calculated and fed into different classifiers. We show good performance on the widely used PTB databases.