Design of a Model for Early Detection of Abnormal Heart Rhythms and Its Severity in Electrocardiogrma Using Deep Learning Techinques

This work presents a deep learning method for evaluating the severity and early identification of irregular cardiac rhythms in electrocardiogram (ECG) readings. The model uses Recurrent Neural Networks (RNNs) to accurately classify normal and pathological rhythms by extracting complex information from ECG data. Additionally, a regression model is used to evaluate the severity of irregular beats, giving physicians important information. The suggested model performs well in tasks involving both detection and severity assessment, according to evaluation using a sizable ECG dataset. Through the provision of a trustworthy mechanism for the preliminary diagnosis and treatment of cardiovascular illnesses, this research advances healthcare technology.