DeepArr: An investigative tool for arrhythmia detection using a contextual deep neural network from electrocardiograms (ECG) signals

In the context of Cardiovascular Diseases, arrhythmia is one of the causes of sudden death, which is related to abnormal electrical activities of the heart that can be reflected by the electrocardiogram (ECG) which plays the main role in heart disease analysis. However, it is still a challenge to detect arrhythmia based on ECG basic characteristics because of the non-stationary nature of ECG signal even cardiologists faced challenges in arrhythmia diagnosis. Therefore, automatic arrhythmia detection-based ECG signals with height accuracy is a serious and indispensable task. Hence In this paper, we propose a new deep learning-based approach called “DeepArr” that uses a sequential fusion method to combine feed-forward and recurrent deep neural networks to exploit relevant features representation of arrhythmia from electrocardiograms (ECG) signals. A comprehensive experimental study has been made in this research, which shows that the proposed approach offers the most efficient tool for accurate classification and ranks top of the list of recently published algorithms on the MIT-BIH arrhythmia dataset. 10-fold cross-validation is carried out. The proposed DeepArr model achieved an accuracy, specificity, sensitivity, precision, and F1-score of 99.46%, 99.57%, 97.01%, 98.26%, and 97.63%, respectively. The proposed model provides a robust tool for the early detection of Arrhythmia. •End-to-end hybrid DNN is proposed without handicraft features.•DeepArr model combines 1-D convolutional and BiLSTM layers to learn features.•The intra-patient paradigm for multi-class classification.