The catastrophic heart diseases such as Myocardial Infarction (MI), Heart Failure (HF) and Ischemic Heart Disease (IHD) is a chain process leads to Coronary Artery Disease (CAD). The analysis of CAD from Electrocardiogram (ECG) signals by manual techniques are quite difficult. Therefore, there is need of techniques without human interaction for classifying the CAD should be improved. This work presented the recognition of five types of ECG beats by using a three-step system. In the first step, Pan-Tompkins algorithm (PTA) is used for detecting the peaks in ECG signals. The second step includes extraction of three interval features combined with ECG higher order statistics. In the third step for classifying ECG beats K-Nearest Neighbour (KNN) technique is employed. This approach analysed the heartbeats as normal or abnormal as accurately, and the experiments were conducted on MIT/BIH arrhythmia database for classifying the ECG signals. The results stated that the accuracy of the proposed approach is up to 98.40% for segregating the signals.

In this research study, arrhythmia classification was assessed by using Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. After signal acquisition, semantic feature extraction (combination of statistical, entropy, linear and non-linear features) was used for extracting the feature values from acquired electrocardiogram (ECG) signals. Here, twenty-six features were combined to perform feature extraction on acquired ECG signals. Then, the infinite feature selection was used for rejecting the irrelevant feature vectors or selecting the optimal feature subsets. After selecting the optimal feature vectors, a binary classifier: random forest was used to classify the eight arrhythmia heartbeat conditions: Normal sinus rhythm (N), Sinoauricular Heart Block (SHB), Premature Ventricular Contraction (PVC), Auricular Fibrillation (AF), Left Bundle Branch Block (LBBB), Premature Atrial Contraction (PAC), Supra-Ventricular-Tachycardia (SVT) and Right Bundle Branch Block (RBBB). This research work includes numerous advantages; earlier detection of arrhythmia diseases, assists clinicians during surgery, and cost efficient compared to the existing machine learning methodologies. The experimental outcome shows that the proposed methodology effectively distinguishes the heartbeat conditions by means of sensitivity, specificity, Negative Predictive Value (NPV), Positive Predictive Value (PPV) and accuracy. The proposed methodology enhances the classification accuracy up to 1.4-1.5% compared to the existing methodologies.