Paper Title
Detection of Abnormal Myocardial Activity Using Pattern Search Algorithm

Abstract
Heart disease is a leading cause of death globally. A type of heart disease, Myocardial Infraction is caused when the blood flow to a portion of heart is decreased or completely blocked which causes muscles to die off from lack of oxygen. Myocardial Infraction is silent and often go undetected which may cause sudden death to the patient. Early Detection of the disease is important to save lives. Electrocardiogram (ECG) is used to monitor heart’s function, any changes in an ECG signal can be a sign of heart-related conditions. In order to find the abnormality in the heart, the ECG signal should be analyzed. In the proposed system, the abnormality in the ECG signal is detected. The ECG data is collected from MIT-BIH arrhythmia dataset. The processed ECG data is statistically mapped by calculating sum of absolute mean (SAM) with every fixed frame of data read through the file read protocol. The comparative analysis is systematically evaluated through pattern search algorithm (PSA) which is the metaphor concept of Whale optimization algorithm (WOA). The data distribution follows the concept of WOA before fetching it into the PSA model. As the system is focused on detection of normal ECG data and MI data, the simulation triggers the notification as text reflecting the disease name. Keywords - Electrocardiogram (ECG), Pattern Search Algorithm, Myocardial Infraction, MIT-BIH.