ArticleScientific reports2025
A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.Scientific reports · 2026Article
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Deep learning-based early prediction of life-threatening ventricular arrhythmias using long-term Holter ECG signals.Frontiers in cardiovascular medicine · 2026Article
- MS-LTCAF: A Multi-Scale Lead-Temporal Co-Attention Framework for ECG Arrhythmia Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Arrhythmia Classification with Single-Channel Features Extracted from "A Large-Scale 12-Lead ECG Database for Arrhythmia Study".Sensors (Basel, Switzerland) · 2025Article
- Efficient Deep Learning-Based Arrhythmia Detection Using Smartwatch ECG Electrocardiograms.Sensors (Basel, Switzerland) · 2025Article
- MAK-Net: A Multi-Scale Attentive Kolmogorov-Arnold Network with BiGRU for Imbalanced ECG Arrhythmia Classification.Sensors (Basel, Switzerland) · 2025Article
- An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization.Scientific reports · 2025Article
- An intelligent diagnosis method for cardiovascular diseases based on the CNN-CBAM-GRU model.PloS one · 2025Article
- High-accuracy ECG image interpretation using parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning with multimodal LLaMA V.3.2.BMJ digital health & AI · 2025Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term dependencies. Unlike traditional CNN-based models that require R-peak detection, the proposed model operates without it and demonstrates superior accuracy and efficiency. The findings contribute to enhancing the accuracy of ECG-based arrhythmia diagnosis and are applicable to real-time monitoring systems. Specifically, the model achieves an accuracy of 97.8% on the Icentia11k dataset using four arrhythmia classes and 99.58% on the MIT-BIH dataset using five arrhythmia classes.
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