SMART ECG-BASED ARRHYTHMIA DETECTION USING ARTIFICIAL INTELLIGENCE

Abstract

Electrocardiogram (ECG) signals provide critical information for diagnosing cardiac arrhythmias, which are abnormal heart rhythms that can lead to severe health complications. Traditional manual analysis of ECG signals is time-consuming and prone to human error. Artificial Intelligence (AI) and machine learning techniques offer automated solutions for accurate and real-time arrhythmia detection. This research proposes a smart AI-based system for analyzing ECG signals and detecting various types of arrhythmias. Signal preprocessing, feature extraction, and classification using deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are implemented. The system is trained and validated on publicly available ECG datasets, including MIT-BIH Arrhythmia Database. Performance metrics, including accuracy, sensitivity, specificity, and F1-score, are evaluated. Experimental results demonstrate that the AI-based model outperforms conventional methods in detection accuracy. This approach enables early detection of lifethreatening arrhythmias, reduces diagnostic time, and supports real-time patient monitoring. The proposed system provides a reliable, scalable, and efficient solution for intelligent cardiac healthcare.