Multi-model Deep Learning Ensemble for ECG Heartbeat Arrhythmia Classification

Managing and treating cardiovascular diseases can be substantially improved by automatic detection and classification of the heart arrhythmia. In this paper, we introduced a novel deep learning system for classifying the electrocardiogram (ECG) signals. The heartbeats are classified into different arrhythmia types using two proposed deep learning models. The first model is integrating the convolutional neural network (CNN) and long short-term memory (LSTM) network to extract useful features within the ECG signal. The second model combines several classical features with LSTM in order to effectively recognize abnormal classes. These deep learning models are trained using a bagging model then aggregated by a fusion classifier to form a robust unified model. The proposed system is evaluated on the MIT-BIH arrhythmia database and produces an overall accuracy of 95.81%, which significantly outperforms the state-of-the-art.