A Multiclass Arrhythmia Classification Method Based on Photoplethysmography Signals of Smartwatches
Year: 2026
Type: article
Abstract: Currently, cardiovascular diseases remain one of the major challenges facing humanity globally. Various types of arrhythmias caused by cardiovascular disease can affect the effective heart contraction and blood ejection, leading to serious complications such as thrombosis. Therefore, convenient detection and rapid classification of arrhythmias is very necessary and has a good application prospect. In this article, we investigate a classification method of multiclass arrhythmia based on photoplethysmography (PPG) signals of smartwatches. The arrhythmia classification method, utilizing the proposed multiscale group convolution and efficient channel attention enhanced residual network and bidirectional gated recurrent unit (MGERB) model, can identify six different types of arrhythmias. By combining the multiscale group convolution and efficient channel attention (MGE) mechanisms, we design a new MGE block to improve the residual blocks of the residual network (ResNet). The spatial features of PPG signals are extracted by stacking the MGE blocks. Subsequently, the feature vectors are input into the bidirectional gated recurrent unit (BiGRU) to extract the temporal features of PPG signals. Finally, the spatial features from the MGE blocks and the temporal features from the BiGRU are fused to classify the types of arrhythmias. Experiments show that the proposed method achieved an overall accuracy of 91.11% on the public dataset and 95.57% on the self-collected dataset, which verifies the accuracy and effectiveness of the proposed method.
Authors: Mao LI, Chengcheng GUO
Institutions: Wuhan College, Wuhan University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204792503