Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

By U. Acharya, Hamido Fujita, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, M. Adam. Information Sciences, 2017

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Deep learning for healthcare applications based on physiological signals: A review (2018, background): “For example, Acharya et al. [88] reported the performance measures obtained through the deep learning system alongside the performance ﬁgures of the traditional diagnosis support systems.”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “CNN was recently employed in the automated diagnosis of myocardial infraction [27] and coronary artery disease [29] using ECG signals.”
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey (2018, methodology): “, [46] aimed at automated detection of abnormalities in ECG signals.”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, neutral): “[23] CNN ECG Automatic detection of myocardial infarction; average accuracy was 93.”
ECG Heartbeat Classification: A Deep Transferable Representation (2018, neutral): “[8]1 93.”