Key result
HOVLSTM framework achieves ~99% arrhythmia classification accuracy, outperforming existing systems.
Why the study?
Detecting and classifying arrhythmias remains challenging due to the complex nature and large volumes of ECG data in IoT-compatible systems.
A novel hybrid deep learning approach combining Vision Transformer and LSTM achieved 99.17% accuracy for automatic arrhythmia classification from ECG signals.
Proposed LSTM-ViT hybrid for ECG arrhythmia detection; leaves open clinical validation before IoT deployment.
Background In electrocardiogram (ECG) signal classification, advanced IoT-compatible systems and medical signal processing solutions have become feasible due to the rapid growth of the Internet of Things (IoT). However, it is challenging to detect and classify arrhythmias because of the complex nature and large volumes of ECG data. Objective: This article proposes a hybrid deep learning (DL) approach by combining long short-term memory (LSTM)and a Vision Transformer (ViT) for the automatic arrhythmia classifi cation from ECG signals. Objective This article proposes a hybrid deep learning (DL) approach by combining long short-term memory (LSTM)and a Vision Transformer (ViT) for the automatic arrhythmia classifi cation from ECG signals. Methods Initially, the proposed framework gathers the data from the MIT-BIH database for training. Then, preprocessing strategies such as min-max normalization and signal-to-spectrogram conversion using the Stockwell transform are applied. Then, the imbalance problem in the collected dataset is rectifi ed by applying the K-Mean scentered Adaptive Synthetic Sampling (KMADASYN) technique. Afterward, the proposed framework employs the Hybrid Optimized ViT with Long Short-Term Memory (HOVLSTM) for classification, where spatial and temporal features are extracted to achieve higher classification performance. Results The developed model achieved a classification accuracy of 99.17%, out performing several existing ECG classification systems. Conclusion These outcomes demonstrate that the proposed technique is suitable for IoT-compatible healthcare systems. While the IoT-compatible system does not employ data acquisition, the model is evaluated in a simulated IoT environment, demonstrating its potential for future deployment in IoT-enabled clinical decision-support systems.
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Birunda et al. (2026) studied Arrhythmia. Hybrid Optimized ViT with Long Short-Term Memory (HOVLSTM) vs. Existing ECG classification systems was evaluated on Classification accuracy. The Hybrid Optimized ViT with Long Short-Term Memory (HOVLSTM) framework achieved an arrhythmia classification accuracy of 99.17%, outperforming several existing systems.
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