Key result
MGERB model achieves ~91% accuracy in classifying six arrhythmias from smartwatch PPG signals.
Why the study?
Arrhythmias cause complications such as thrombosis by affecting heart contraction and blood ejection, making convenient detection and rapid classification necessary.
A novel deep learning model (MGERB) demonstrates high accuracy in classifying six types of arrhythmias using smartwatch PPG signals, highlighting its potential for convenient, non-invasive arrhythmia monitoring.
Answers are grounded in this paper and cite their sources.
Supports wearable arrhythmia screening development; leaves open prospective clinical validation before adoption.
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.
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A 2026 study studied Arrhythmias (n=91). MGERB model vs. Other deep learning models was evaluated on Overall classification accuracy. The proposed MGERB model achieved an overall accuracy of 91.11% on the public dataset and 95.57% on the self-collected dataset for classifying six types of arrhythmias using smartwatch PPG signals.
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