Key result
A multi-channel automatic classification algorithm combining a 16-layer CNN and LSTM achieved an accuracy of 95.4%, sensitivity of 98.2%, and specificity of 86.5% for myocardial infarction ECGs.
Why the study?
Wearable and portable ECG devices enable timely myocardial infarction detection, motivating the development of automated algorithms to classify myocardial infarction ECG signals without complex handcrafted features.
Answers are grounded in this paper and cite their sources.
A combined CNN and LSTM algorithm can accurately classify myocardial infarction from I-lead ECG signals without requiring complex handcrafted features.
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.
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Feng et al. (2019) studied Myocardial infarction. Multi-channel automatic classification algorithm combining a 16-layer CNN and LSTM was evaluated on Classification performance (accuracy, sensitivity, specificity, F1 score). A multi-channel automatic classification algorithm combining a 16-layer CNN and LSTM achieved an accuracy of 95.4%, sensitivity of 98.2%, and specificity of 86.5% for myocardial infarction ECGs.
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