An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm.

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to the blood flow dynamics leads to the occurrence of cardiac arrhythmias in the cardiac system. In this work, estimation of cardiac output (CO) parameter using blood flow rate analysis is carried out, which is a vital parameter to identify the subjects with left- ventricular arrhythmias (LVA). In particular, LVA is a resultant component of characteristic changes in blood rheology (blood flow rate). The CO is an intrinsic parameter derived from the stroke volume (SV) characterized by end-diastolic/systolic volumes (EDV/ESV) and heart rate. The pumping of blood from left ventricle (LV) reconciles in to R-R intervals depicted on ECG, which are used for heart rate estimation. The deviation from the nominal values of CO implies that, the subject is more prone to LVA. Further, the identification of subjects with LVA is accomplished by computing the features from the ECG signals. The proposed Feature Ranking Score (FRS) algorithm employs different statistical parameters to label the score of the extracted features. The feature score enables the selection optimal features for classification. The optimal features are further given to the Least Square- Support Vector Machine (LS-SVM) classifier for training and testing phases. The signals are acquired from public domain MIT-BIH arrhythmia database, used for validating the proposed technique for identifying the LVA using blood flow.