Effective learning and filtering of faulty heart-beats for advanced ECG arrhythmia detection using MIT-BIH database

Abstract

Electrocardiogram (ECG) signal has been established as one of the most fundamental bio-signals for monitoring and assessing the health status of a person. ECG analysis flow relies on the detection of points of interest on the signal with the QRS complex, located around an R peak of the heart beat, being the most commonly used. Using the MIT-BIH arrhythmia database, we evaluate the accuracy of various R peak detectors, showing a large number, i.e. several thousands, of falsely detected peaks. Considering the medical significance of the ECG analysis, we propose a machine learning based classifier to be incorporated in the ECG analysis flow aiming at identifying and discarding heart beats based on erroneously detected R peaks. Using Support Vector Machines (SVMs) and extensive exploration, we deliver a tuned classifier that i) successfully filters up to 75% of the false beats, ii) while keeping the correct beats mis-classified as false lower than 0.01% and iii) the computational overhead of the classifier sufficiently low.