ArticleiScience2024
Heartbeat classification method combining multi-branch convolutional neural networks and transformer.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks.Scientific reports · 2025Article
- Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier.Sensors (Basel, Switzerland) · 2025Article
- Personalized Clustering for Emotion Recognition Improvement.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The detection and classification of arrhythmias are crucial steps in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often fail to consider both the morpho-logical and temporal features of the electrocardiogram (ECG) simultaneously. Therefore, we propose a hybrid heartbeat classification method that combines Transformer and multi branch convolutional neural networks (CNNs). Then, use the fusion module to stitch the features obtained from different classifiers. We performed three different heartbeat classification protocols on the MIT-BIH arrhythmia (MIT-BIH-AR) database and analyzed performance on SVEB and VEB classes to validate our method. The first was an intra-patient protocol with an
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