Abstract
Myocardial infarction is one of the most threatening cardiovascular diseases for human beings. With the rapid development of wearable devices and portable electrocardiogram (ECG) medical devices, it is possible and conceivable to detect and monitor myocardial infarction ECG signals in time. This paper proposed a multi-channel automatic classification algorithm combining a 16-layer convolutional neural network (CNN) and long-short term memory network (LSTM) for I-lead myocardial infarction ECG. The algorithm preprocessed the raw data to first extract the heartbeat segments; then it was trained in the multi-channel CNN and LSTM to automatically learn the acquired features and complete the myocardial infarction ECG classification. We utilized the Physikalisch-Technische Bundesanstalt (PTB) database for algorithm verification, and obtained an accuracy rate of 95.4%, a sensitivity of 98.2%, a specificity of 86.5%, and an F1 score of 96.8%, indicating that the model can achieve good classification performance without complex handcrafted features.
Highlights
Myocardial infarction is a cardiovascular disease caused by myocardial insufficient blood supply or even myocardial necrosis due to coronary artery occlusion
In the field of ECG signal processing, many traditional studies have focused on the feature extraction of myocardial infarction ECG signals including time domain, frequency domain, wavelet transform, and other characteristics
Of multi-lead ECG signals, myocardial infarction classification can be achieved with a high recognition rate through support vector machine (SVM), K-nearest neighbor (KNN), and other methods
Summary
Myocardial infarction is a cardiovascular disease caused by myocardial insufficient blood supply or even myocardial necrosis due to coronary artery occlusion. Health Association, nearly 720,000 Americans suffer from myocardial infarction each year [1]. In the early stage of this disease, patients with myocardial infarction usually show symptoms such as chest pain and chest tightness, but some patients still have no obvious symptoms, which makes it difficult to treat in time, threatening life [2]. Electrocardiogram (ECG) is one of the routine examination methods for myocardial infarction [2]. In the field of ECG signal processing, many traditional studies have focused on the feature extraction of myocardial infarction ECG signals including time domain, frequency domain, wavelet transform, and other characteristics. Sun et al extracted ST segments and combined support vector machine (SVM) and multi-instance learning to complete myocardial infarction ECG classification [3].
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