Integrated portable ECG monitoring system with CNN classification for early arrhythmia detection.

Introduction: Electrocardiograms (ECGs) play a crucial role in diagnosing heart diseases by capturing the electrical activity of the heart. With the rising need for real-time cardiac monitoring, portable solutions have gained significance for timely detection and intervention. This study presents a portable ECG monitoring system incorporating Convolutional Neural Networks (CNNs) for accurate classification of cardiac abnormalities, including arrhythmias. Methods: The proposed system consists of an Arduino Nano microcontroller interfaced with an AD8232 ECG sensor for real-time ECG signal acquisition. The collected ECG data undergoes preprocessing before being fed into CNN models trained on the MIT-BIH Arrhythmia dataset. The model is designed for both binary and multi-class classification, distinguishing normal and abnormal heart rhythms. Performance metrics, including accuracy, were evaluated against state-of-the-art approaches to assess classification effectiveness. Results: Experimental evaluations demonstrate the CNN model's high classification accuracy, achieving 98.35% in binary classification and 99.3% in multi-class classification. These results surpass existing benchmarks, highlighting the efficiency of the proposed system. The system's low-cost hardware and real-time classification capabilities enhance its suitability for continuous cardiac monitoring. Discussion: The proposed ECG monitoring system presents a reliable and cost-effective solution for early arrhythmia detection. By leveraging CNNs, it ensures accurate classification of cardiac abnormalities, making it a promising tool for both clinical and remote healthcare settings. Its potential impact extends to real-time monitoring, early diagnosis, and personalized healthcare, contributing to improved cardiovascular health management.