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A deep convolutional neural network model to classify heartbeats
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network
Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads
Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
Automated EEG-based screening of depression using deep convolutional neural network
Deep learning for healthcare applications based on physiological signals: A review
Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network
Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals
The proposed 11-layer deep convolutional neural network (CNN) model can be put into practice and serve as a diagnostic aid for cardiologists by providing more objective and faster interpretation of ECG signals.