ArticleBiomedical engineering and computational biology2025
An Enhanced Hybrid Model Combining CNN, BiLSTM, and Attention Mechanism for ECG Segment Classification.
Article in Biomedical engineering and computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.Scientific reports · 2026Article
- ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.Scientific reports · 2026Article
- HAT-ECG: Hybrid autoencoder-transformer architecture for ECG arrhythmia classification.Physiological reports · 2026Article
- Real-Time Cardiac Arrhythmia Classification Using TinyML on Ultra-Low-Cost Microcontrollers: A Feasibility Study for Resource-Constrained Environments.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- A 1-Dimensional Physiological Signal Prediction Method Based on Composite Feature Preprocessing and Multi-Scale Modeling.Sensors (Basel, Switzerland) · 2025Article
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2 authors.
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Abstract
Deep learning models are necessary in the field of healthcare for the diagnosis of cardiac rhythm diseases since the conventional ECG classification is based on hand-crafted feature engineering and traditional machine learning. Nevertheless, CNN and BiLSTM architectures provide automatic feature learning, enhancing ECG classification accuracy. The current research work puts forward a framework integrating CNN with CBAM and BiLSTM layers for the purpose of extracting valuable features and classifying ECG signals. The model classifies heartbeats according to the AAMI EC57 standard into 5 categories: normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). To tackle uneven class distributions, SMOTE synthesizes new samples, making the model more robust. Evaluation on MIT-BIH arrhythmia database yields remarkable results with 99.20% accuracy, 97.50% sensitivity, 99.81% specificity, and 98.29% mean
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