Electrocardiogram (ECG), a non-invasive technique is used as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. The detection of cardiac arrhythmias in the ECG signal consists of detection of QRS complex in ECG signal, feature extraction from detected QRS complexes and classification of beats using extracted feature set from QRS complexes. Transmission of signals across public receiver networks is another request in which large amount of data is implicated. For both detection and transmission in ECG signal, data compression is an important operation and represents another purpose of ECG signal processing. Hence, in this research work, the best training method i.e. Levenberg Marquardt algorithm will be utilized for classification on the basis of validation checks or epochs with an optimization technique. The purpose of this research work is to classify the disease dataset using Bacterial Foraging Optimization (BFO) Algorithm and trained by Levenberg Marquardt algorithm on the basis of the features extracted and also to test the image on the basis of the features at the database and the features extracted of the waveform, will be tested. The advantage of proposed method is to minimize the error rate of the classification which occurs due to insignificant count of R-peaks.