D-Convolutional Neural Network Based-Arrhythmia Classification Model

This paper proposes an arrhythmia classification system that combines 1D Convolutional Neural Network with three selected temporal features: QRS duration, and the current and next RR intervals. Our aim is to leverage the advantages of deep learning, which allows for automatic feature extraction, while also utilizing some crucial hand-crafted features. We tested the effectiveness of this model with the MIT-BIH Arrhythmia Dataset, adhering to the guidelines provided by the Association for the Advancement of Medical Instrumentation. The proposed hybrid approach demonstrates good performance in terms of accuracy, sensitivity, and specificity, while maintaining a simpler structure.