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Akbari Poodineh, Motahareh, Zare Mehrjardi, Fatemeh, Sardari Zarchi, Mohsen. (1404). Diagnosis of Cardiac Arrhythmia using an Optimized Two-stage Deep Learning Model. فناوری آموزش, (), -. doi: 10.22061/jecei.2026.12629.898
Motahareh Akbari Poodineh; Fatemeh Zare Mehrjardi; Mohsen Sardari Zarchi. "Diagnosis of Cardiac Arrhythmia using an Optimized Two-stage Deep Learning Model". فناوری آموزش, , , 1404, -. doi: 10.22061/jecei.2026.12629.898
Akbari Poodineh, Motahareh, Zare Mehrjardi, Fatemeh, Sardari Zarchi, Mohsen. (1404). 'Diagnosis of Cardiac Arrhythmia using an Optimized Two-stage Deep Learning Model', فناوری آموزش, (), pp. -. doi: 10.22061/jecei.2026.12629.898
Akbari Poodineh, Motahareh, Zare Mehrjardi, Fatemeh, Sardari Zarchi, Mohsen. Diagnosis of Cardiac Arrhythmia using an Optimized Two-stage Deep Learning Model. فناوری آموزش, 1404; (): -. doi: 10.22061/jecei.2026.12629.898

Diagnosis of Cardiac Arrhythmia using an Optimized Two-stage Deep Learning Model

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 اسفند 1404
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2026.12629.898
نویسندگان
Motahareh Akbari Poodineh؛ Fatemeh Zare Mehrjardi؛ Mohsen Sardari Zarchi*
Department of Computer Engineering, Meybod University, Meybod, Iran.
تاریخ دریافت: 27 مهر 1404،  تاریخ بازنگری: 29 بهمن 1404،  تاریخ پذیرش: 04 اسفند 1404 
چکیده
Background and Objectives: Cardiovascular diseases, particularly cardiac arrhythmias, are among the leading causes of mortality worldwide. Early and accurate diagnosis is essential for improving patient outcomes. Although electrocardiogram (ECG) signals are widely used for arrhythmia detection, manual interpretation remains time consuming and error prone. Therefore, this study proposes an innovative, optimized two stage deep learning framework for the reliable diagnosis of cardiac arrhythmias from ECG signals, aiming to enhance both accuracy and robustness.
Methods: The key innovation lies in the first stage, where the autoencoder’s reconstruction error threshold is optimized using a Genetic Algorithm (GA) to maximize the separation between normal and abnormal signals. Only signals identified as abnormal proceed to the second stage, a Convolutional Neural Network (CNN) that classifies them into four arrhythmia types (Supraventricular, Ventricular, Fusion, and Unknown beats). All experiments were conducted on the MIT BIH Arrhythmia Database using a stratified split, with SMOTE applied exclusively to the CNN training set to address class imbalance. Performance was evaluated through 5 fold cross validation.
Results: The proposed AE GA CNN+SMOTE framework achieved an average accuracy of 97.89 ± 0.25%, precision of 97.90 ± 0.24%, recall of 97.68 ± 0.29%, and an F1 score of 97.69 ± 0.28%. It outperformed the single stage CNN+SMOTE baseline by +6.28% in accuracy (p < 0.001) and showed statistically significant improvements over all other two stage variants (p < 0.05).
Conclusion: The two stage architecture, enhanced by GA driven threshold optimization and SMOTE balancing, demonstrates high accuracy and robustness for automated arrhythmia screening. The statistically validated performance gains support its potential as a decision support tool for clinical and real time ECG analysis.
کلیدواژه‌ها
Cardiac arrhythmia؛ Autoencoder؛ Convolutional Neural Network؛ ECG Signal؛ Genetic Algorithm
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