Early Detection of Heart Diseases through Deep Learning–Based Electrocardiogram (ECG) Analysis
Year: 2025
Type: article
Abstract: Cardiovascular diseases are among the leading causes of death worldwide, and early diagnosis plays a critical role in reducing mortality rates. Electrocardiogram (ECG) signals are a fundamental diagnostic tool for identifying arrhythmias; however, manual interpretation requires expert knowledge and is prone to diagnostic errors. In this study, a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks was developed for automatic arrhythmia detection from ECG signals. The model was trained using the MIT-BIH Arrhythmia Database, and several preprocessing steps—including noise removal, windowing, standardization, and SMOTE-based data balancing—were applied. The proposed CNN-LSTM model achieved high performance in five-class (N, S, V, F, Q) arrhythmia classification, with an accuracy of 95.2%, an F1 score of 92%, and a loss value of 0.14. The confusion matrix results indicate that the model demonstrates strong generalization capability, particularly for the N, V, and Q classes. These findings suggest that the hybrid architecture can serve as an effective decision-support tool in ECG signal analysis. Furthermore, integrating the proposed system into portable devices may enable real-time and automated monitoring of cardiac rhythms; however, the model has not been approved by regulatory agencies such as the FDA or CE, and further validation is required before clinical use. Additionally, by reducing the diagnostic burden on clinicians, the system has the potential to improve both accuracy and efficiency within clinical workflows. Future studies incorporating cross-dataset validation and larger ECG collections may further strengthen the reliability and robustness of the proposed approach.
Authors: Ataş, İsa, Alhajahmad, Bashar, Ataş, Musa
Institutions: Dicle University, Siirt Üniversitesi
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7217828756