Key result
Hybrid transformer and transfer learning model achieves ~99% accuracy in detecting ECG arrhythmias.
Why the study?
Evaluating ECG signals to detect and classify heart arrhythmias is time-consuming and open to observer-related errors, motivating the development of improved deep learning-based approaches to automatically detect rhythm disorders and reduce errors.
Does a hybrid transformer and transfer learning approach improve classification accuracy of heart rhythm disorders (normal, abnormal, tachycardia, bradycardia) from ECG signals?
Does a hybrid transformer and transfer learning approach improve classification accuracy of heart rhythm disorders (normal, abnormal, tachycardia, bradycardia) from ECG signals?
A hybrid transformer and transfer learning model achieved up to 98.98% accuracy in classifying ECG signals into normal, abnormal, tachycardia, and bradycardia categories.
Answers are grounded in this paper and cite their sources.
Requires prospective clinical validation before use; leaves open hybrid models' role in arrhythmia detection.
Background Heart arrhythmias are one of the leading causes of death worldwide. Electrocardiography (ECG) is used to detect and classify these arrhythmias. ECG provides a mechanism that offers comprehensive information about the heart through its electrical activity. It does not harm the patient and is cost-effective. However, the evaluation process is time-consuming and open to observer-related errors. Improving deep learning-based approaches for detecting normal, abnormal, tachycardia, and bradycardia conditions that occur through ECG signals is a gain in reducing errors. Objective This study investigates the effectiveness of a deep learning-based architecture in automatically detecting and classifying heart rhythm disorders from ECG signals. Methods An original dataset was created with four categories (normal, abnormal, tachycardia, and bradycardia), including regular and abnormal heartbeats. The research evaluates the potential to increase the accuracy and efficiency of arrhythmia detection by hybrid use of transformer and transfer learning approaches, and the proposed normalization techniques. Results The proposed hybrid model achieved a classification accuracy of up to 98.98% on the test dataset. Conclusion These findings highlight the potential of AI-enabled systems to revolutionize ECG analysis and provide a robust, cost-effective, and scalable solution for early diagnosis and treatment in cardiology. This research is a significant contribution to the application of AI in healthcare. It is also critical for improving interdisciplinary collaboration and patient-centered diagnostic tools.
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Akalın et al. (2026) studied Heart arrhythmias (normal, abnormal, tachycardia, and bradycardia). Hybrid transformer and transfer learning deep learning model with normalization techniques was evaluated on Classification accuracy for detecting and classifying heart rhythm disorders from ECG signals. A hybrid transformer and transfer learning model achieved up to 98.98% classification accuracy in detecting arrhythmias (normal, abnormal, tachycardia, and bradycardia) from ECG signals.
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