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.
Population
ECG signals from the Physikalisch-Technische Bundesanstalt (PTB) database
Design
Algorithm development and validation study
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.
Authors
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Does not yet support clinical adoption of CNN-LSTM MI classifiers; leaves open prospective validation.
A combined CNN and LSTM algorithm can accurately classify myocardial infarction from I-lead ECG signals without requiring complex handcrafted features.
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.