Key result
A patient-specific reconstruction method using single-lead derived vectorcardiography and machine learning achieved satisfactory performance in differentiating MI patients from healthy subjects.
Why the study?
Can an automatic classification system using single-lead derived VCG and LSTM network accurately diagnose and localize myocardial infarction?
Can an automatic classification system using single-lead derived VCG and LSTM network accurately diagnose and localize myocardial infarction?
A novel machine learning approach using single-lead derived vectorcardiography shows promise for automated detection and localization of myocardial infarction.
May aid single-lead MI screening development; leaves open prospective validation before any clinical use.
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.
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Chuang et al. (2020) studied Myocardial infarction. Spline representation of single-lead derived vectorcardiography with LSTM and multilayer perceptron network vs. Healthy subjects was evaluated on Differentiating MI patients from healthy subjects and localizing the infarcted area. A patient-specific reconstruction method using single-lead derived vectorcardiography and machine learning achieved satisfactory performance in differentiating MI patients from healthy subjects.
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