Automatic classification of myocardial infarction using spline representation of single-lead derived vectorcardiography

Yu Hung Chuang, Chia Ling Huang, Wen Whei Chang*, Jen Tzung Chien

*Corresponding author for this work

研究成果: Article同行評審

摘要

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra-and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area.

原文English
文章編號7246
頁(從 - 到)1-18
頁數18
期刊Sensors (Switzerland)
20
發行號24
DOIs
出版狀態Published - 2 十二月 2020

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