ArticleFrontiers in physiology2023
Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion.
Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- HPRNet: a hierarchical pyramidal residual network for ECG arrhythmia classification.Frontiers in physiology · 2026Article
- Cardiac arrhythmias classification using photoplethysmography database.Scientific reports · 2024Article
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5 authors.
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Abstract
Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices.
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