International Journal of Engineering and Technology

The rife of heart disease (HD) is a comprehensive phenomenon, and the scale of the cardiovascular disease (CVD) increases in prevalence in the developed world. Cardio vascular disease (CVD) is the foremost cause of death worldwide; the World Health Organization (WHO) estimates that globally 17.3 million people died from Heart Disease in 2008, representing 30% of global deaths. The forecast of heart disease is a multi-layered problem, which is not free from false assumptions. The eminence of the clinical decisions and the effect of the stratagems should optimize the patient’s outcomes and to lessen the risk of disease factors, if the methods are applied effectively and properly grounded on the expert analysis on the presented data. The major clinical information related to heart disease can be obtained by the analysis for electrocardiograph (ECG) signal. The ST segment Myocardial Infarction (STEMI) is the severe type and the elevated ST segment on the ECG data represents that large amount of heart muscle mutilation is stirring. In this paper we recommend a constructive approach to identify the STEMI in the ECG signal of a person. The sample ECG data’s are acquired from the MIT-BIH databases. Those data’s are subsequently pre-processed; the ST segment is extracted and then measured to identify the availability of the disease. During the ST segment analysis stage the beats generated by the ventricular in origin or ventricular paced are resolute. The fine-tuned data set is converted into a formatted data set and conceded to the Bacterial Foraging Optimization Algorithm (BFOA) to detect the approximate solution. The proposed system overcomes the superseded algorithms by a focussed update in the methodology with reliable algorithms and techniques.