Research on Multi-Scale Parallel Joint Optimization CNN for Arrhythmia Diagnosis

Abstract

The morphological characteristics of electrocardiograms (ECGs) serve as a fundamental basis for diagnosing arrhythmias. Convolutional neural networks (CNNs), leveraging their local receptive field properties, effectively capture the morphological features of ECG signals and have been extensively employed in the automatic diagnosis of arrhythmias. However, the variability in the duration of ECG morphological features renders single-scale convolutional kernels inadequate for fully extracting these features. To address this limitation, this study proposes a multi-scale parallel joint optimization convolutional neural network (MPJO_CNN). The proposed method utilizes convolutional kernels of varying scales to extract ECG features, further refining these features via parallel computation and implementing a joint optimization strategy to enhance classification performance. Experimental results demonstrate that on the MIT-BIH arrhythmia database, this method not only achieved state-of-the-art performance, with an accuracy of 99.41% and an F1 score of 98.09%, but also showed high sensitivity to classes with fewer samples.