A dynamic learning-based ECG feature extraction method for myocardial infarction detection. (30th December 2022)

• Record Type: Journal Article
• Authors: Sun, Qinghua; Xu, Zhanfei; Liang, Chunmiao; Zhang, Fukai; Li, Jiali; Liu, Rugang; Chen, Tianrui; Ji, Bing; Chen, Yuguo; Wang, Cong
• Is Part Of: Physiological measurement. Volume 43:Number 12(2022)
• Publication Date: 2022-12-30
• Subjects: dynamic learning -- dynamic feature -- electrocardiogram -- hybrid feature selection -- myocardial infarction; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/acaa1a
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective. Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight. Approach. To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set. Main results. Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%. Significance. The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.