Naïve Bayes Classifier for ECG Abnormalities Using Multivariate Maximal Time Series Motif

Analyze or diagnose of Cardiovascular activity under abnormal heart beat is extremely an intricate and vital job to the medical experts, made more complicated to a novice persons. Electrocardiogram is a way to measure or diagnose for research on human beings to spot heart disease by abnormal heart rhythms. These streaming medical signals can be well analyzed or diagnosed only with the prior knowledge. This paper proposes the methodology; Multivariate Maximal Time Series Motif with Naïve Bayes Classifier to classify the ECG abnormalities. The proposed model of predicting Time Series Motif is evaluated with the dataset contains the collection of ECG signals of patients recorded using Holter Monitor. The efficiency of the proposed work is proved by comparing the precision of existing with various Feature extraction Techniques.