A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs

Abstract

Objective. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals. Approach. In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model’s performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs. Main results. As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge). Significance. A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive–negative class imbalance and improve the model’s generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

“… [105] (2021) ResNet PhysioNet-2021 A ResNet deep neural network architecture with a multi-head attention mechanism foe accurate ECG classification. [106] (2022) 2D-CNN PhysioNet-2021 A multi-scale deep neural network with weighted focal loss for ECG arrhythmias classification on varying dimensional inputs. [107] (2021) Boosting PhysioNet-2021 A mel-frequency cepstrum and amplitude-time heart variability features, handcrafted for ECG arrhythmia classification.…”

“…Xia et al [106] recently proposed a novel multi-scale 2D CNN that can accurately identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECG signals. Furthermore, they demonstrated that reduced-lead models could achieve comparable classification performance to the standard 12-lead model.…”