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Intricating cardiac complexities are the primary factor associated withhealthcare costs and the highest cause of death rate in the world. However,preventive measures like the early detection of cardiac anomalies can preventsevere cardiovascular arrests of varying complexities and can impose asubstantial impact on healthcare cost. Encountering such scenarios usually theelectrocardiogram (ECG or EKG) is the first diagnostic choice of a medicalpractitioner or clinical staff to measure the electrical and muscular fitnessof an individual heart. This paper presents a system which is capable ofreading the recorded ECG and predict the cardiac anomalies without theintervention of a human expert. The paper purpose an algorithm which read andperform analysis on electrocardiogram datasets. The proposed architecture usesthe Discrete Wavelet Transform (DWT) at first place to perform preprocessing ofECG data followed by undecimated Wavelet transform (UWT) to extract ninerelevant features which are of high interest to a cardiologist. Theprobabilistic mode named Bayesian Network Classifier is trained using theextracted nine parameters on UCL arrhythmia dataset. The proposed systemclassifies a recorded heartbeat into four classes using Bayesian Networkclassifier and Tukey's box analysis. The four classes for the prediction of aheartbeat are (a) Normal Beat, (b) Premature Ventricular Contraction (PVC) (c)Premature Atrial Contraction (PAC) and (d) Myocardial Infarction. The resultsof experimental setup depict that the proposed system has achieved an averageaccuracy of 96.6 for PAC\% 92.8\% for MI and 87\% for PVC, with an averageerror rate of 3.3\% for PAC, 6\% for MI and 12.5\% for PVC on realelectrocardiogram datasets including Physionet and European ST-T Database(EDB).

Detection and Prediction of Cardiac Anomalies Using Wireless Body Sensors and Bayesian Belief Networks