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Arrhythmia is one of the most persistent chronic heart diseases in the elderly and is associated with high morbidity and mortality such as stroke, cardiac failure, and coronary artery diseases. It is significant for patients with arrhythmias to automatically detect and classify arrhythmia heartbeats using electrocardiogram (ECG) signals. In this paper, we develop three robust deep convolutional neural network (DCNN) models, including a plain-CNN network and two MSF(multi-scale fusion)-CNN architectures (A and B), to aid in better feature extraction for the detection of arrhythmia and thus significantly improve the performance metrics. The proposed models are trained and tested with a public MIT-BIH arrhythmia database on five types of signals. Six groups of ablation experiments are conducted to analyze the performance of the models. The accuracy, sensitivity, and specificity obtained from MSF-CNN architecture A are higher than those from the plain-CNN model, demonstrating that the different parallel group convolution blocks (1 &#x00D7; 3, 1 &#x00D7; 5, and 1 &#x00D7; 7) dramatically improve a model's performance. Additionally, the best model MSF-CNN architecture B achieves an average accuracy, sensitivity, and specificity of 98.00%, 96.17%, and 96.38%, respectively. This illustrates the method with residual learning and concatenation group convolution blocks has a profound effect on the feature learning of the model. The results of ablation experiments show that our proposed biometric recognition and diagnosis network with residual learning (MSF-CNN B) achieves a rapid and reliable diagnosis approach on ECG signal classification, which has the potential for introduction into clinical practice as an excellent tool for aiding cardiologists in reading ECG heartbeat signals.

A Deep Biometric Recognition and Diagnosis Network With Residual Learning for Arrhythmia Screening Using Electrocardiogram Recordings