Intricating cardiac complexities are the primary factor associated with healthcare costs and the highest cause of death rate in the world. However, preventive measures like the early detection of cardiac anomalies can prevent severe cardiovascular arrests of varying complexities and can impose a substantial impact on healthcare cost. Encountering such scenarios usually the electrocardiogram (ECG or EKG) is the first diagnostic choice of a medical practitioner or clinical staff to measure the electrical and muscular fitness of an individual heart. However, the advancement and the foster development of wearable devices and ECG sensors make it possible to continuously measure and analyze the electrocardiogram and myocardium signals of the heart. These advancements are also making the early detection of cardiac anomalies useful and alert the respective individual and clinicians to provide better healthcare services. The ECG test is the more straightforward test to perform and generally, the clinicians' record this test after encountering the symptoms related to the heart. However, this test requires significant training and adroitness to interpret the recorded ECG correctly and effectively. This paper presents a system which is capable of reading the recorded ECG and predict the cardiac anomalies without the intervention of a human expert. The paper purpose an algorithm which read and perform analysis on electrocardiogram datasets. The proposed architecture uses the Discrete Wavelet Transform (DWT) at first place to perform preprocessing of ECG data followed by undecimated Wavelet transform (UWT) to extract nine relevant features which are of high interest to a cardiologist. The probabilistic mode named Bayesian Network Classifier is trained using the extracted nine parameters on UCL arrhythmia dataset. The proposed system classifies a recorded heartbeat into four classes using Bayesian Network classifier and Tukey's box analysis. The four classes for the prediction of a heartbeat are (a) Normal Beat, (b) Premature Ventricular Contraction (PVC) (c) Premature Atrial Contraction (PAC) and (d) Myocardial Infarction. The results of experimental setup depict that the proposed system has achieved an average accuracy of 96.6 for PAC% 92.8% for MI and 87% for PVC, with an average error rate of 3.3% for PAC, 6% for MI and 12.5% for PVC on real electrocardiogram datasets including Physionet and European ST-T Database (EDB). * Use footnote for providing further information about author (webpage, alternative address)-not for acknowledging funding agencies.
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