Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) commonly used to identify cardiovascular diseases. The professionals can determine the existence of diseases using morphological patterns ECG signals. In order raise diagnostic accuracy and reduce time, automated computer aided diagnosis model necessary. With advancements artificial intelligence (AI) techniques, large quantity datasets be easily examined decision making. this aspect, paper presents intelligent processing (IBECG-SP) technique CVD diagnosis. proposed IBECG-SP examines signals addition, gated recurrent unit (GRU) feature extraction Moreover, earthworm optimization (EWO) algorithm utilized optimally tune hyperparameters GRU model. Lastly, softmax classifier employed allot appropriate class labels applied For examining enhanced outcomes technique, extensive simulation analysis take place on PTB-XL database. experimental results portrayed supremacy over recent state art techniques.