International Research Journal of Education and Technology

The project is a machine learning-based approach for ECG signal classification to detect arrhythmias in support of cardiac health diagnostics. Electrocardiogram signals are preprocessed to remove noise and then transformed into meaningful features to train the model. Based on datasets such as the MIT-BIH Arrhythmia Database, a classification algorithm has been implemented to distinguish between normal and abnormal heart rhythms with high accuracy. The solution is scalable for real- time monitoring, wearable devices, and healthcare diagnostics with a reliable and efficient alternative to manual ECG interpretation.

Keywords: ECG classification, arrhythmia detection, machine learning, signal processing, healthcare diagnostics, MIT-BIH Arrhythmia Database, feature extraction, real-time monitoring.