Cardiac Diagnosis System For Heart Diseases Classification Based on Deep Learning and Optimization Strategies Using ECG Signals

Heart disease is among the leading causes of death worldwide. Significant factors include other cardiovascular disorders, such as aortic disease, coronary artery disease, and arrhythmia. The increase in death tolls has underlined the urgent necessity for precise and timely diagnosis to help initiate proper treatment. A mistake in diagnosis can result in very dire consequences. It is essential to be accurate when determining heart conditions. Lives are saved by early detection. Heart conditions are typically diagnosed using techniques that include electrocardiograms and computed tomography. Here, of the two methods, ECG is essentially the basis of diagnosing heart conditions. However, their interpretation in manual mode involves much time consumption. Traditional diagnosis through signal processing techniques includes feature extraction, selection, and classification that often lacks selection of proper features for better diagnosis accuracy. Deep Learning has been very promising in the last few years for tasks of prediction and classification. This work proposes CNNs to classify heart diseases. The system analyzes ECG signals, focusing on parameters like heart rate and RR intervals, to diagnose conditions such as arrhythmia, coronary artery disease, and aortic disease. The Osprey Optimization Algorithm will enhance the accuracy of the model developed in classifying heart disease by means of ECG. For testing and training purposes, it will evaluate performance with various metrics such as accuracy, sensitivity, specificity, MSE, and PSNR. This strategy is used in order to increase diagnostic accuracy and thus allow early detection while reducing death rates.