Abstract-Diagnosis of heart disease is complex. ECG plays an important role for analysis and diagnosis of heart disease. Normally ECG signals are affected by different noises. These noises pollute the ECG signal. For quality diagnosis it is necessary to de noise the ECG signal. After de noising ECG signals, a pure signal is used to detect ECG parameters. Detection of ECG parameters takes an important role in the analysis of ECG signal. The Feature extracted ECG signal applied to ANN for classification to detect cardiac arrhythmia. This paper introduces the Electrocardiogram (ECG) pattern recognition method based on wavelet transform and neural network technique with error back propagation method has been used to classify two different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle Branch block (RBBB) with normal ECG signal. The MIT-BIH arrhythmias ECG Database has been used for training and testing our neural network based classifier. The simulation results shown at the end.

Fatal ventricular arrhythmias and heart failure are the common modes of death in patients with cardiovascular diseases. Intracardiac defibrillator (ICD) implantation reduces arrhythmic mortality to a significant extent in the high risk patient. However, there continues to be a need for effective drug therapy to reduce the arrhythmic and overall mortality in patients with or without an ICD. Although anti-arrhythmic drugs (AAD) appear inferior to ICD, the role of beta-blockers and to an extent amiodarone along with non AAD like angiotensin converting enzyme inhibitors (ACE-I), mineralocorticoid blockers (MRB) and HMG-CoA reductase inhibitors (statins) need to be emphasized. There have been many drug trials and meta-analysis to this effect and we review the role of drugs especially in their ability to reduce arrhythmic mortality and sudden cardiac death (SCD). The focus is on post myocardial infarction (MI) and heart failure patients with a brief overview of role of drugs in channelopathies.

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