A real time QRS complex classification method using Mahalanobis distance

Abstract

An unsupervised method to recognize and classify QRS complexes was developed in order to create an automatic cardiac beat classifier in real time. After exhaustive analysis, four features extracted from the QRS complex in the time domain were selected as the ones presenting the best results: width, total sum of the areas under the positive and negative curves, total sum of the absolute values of sample variations and total amplitude. Preliminary studies indicated these features follow a normal distribution, allowing the use of the Mahalanobis distance as their classification criterion. After an initial learning period, the algorithm extracts the four features from every new QRS complex and calculates the Mahalanobis distance between its feature set and the centroids of all existing classes to determine the class in which the new QRS belongs to. If a predefined distance is surpassed, a new class is created Using 44 records from the MIT-BIH we have obtained 90,74% of sensitivity, 96,55% of positive predictivity and 0.242% of false positives.