Research Article
Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database
@INPROCEEDINGS{10.4108/eai.14-10-2015.2261640, author={Vasileios Tsoutsouras and Dimitra Azariadi and Sotirios Xydis and Dimitrios Soudris}, title={Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database}, proceedings={5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"}, publisher={ACM}, proceedings_a={MOBIHEALTH}, year={2015}, month={12}, keywords={ecg analysis support-vector-machine (svm) machine learning false heart-beat filtering}, doi={10.4108/eai.14-10-2015.2261640} }- Vasileios Tsoutsouras
Dimitra Azariadi
Sotirios Xydis
Dimitrios Soudris
Year: 2015
Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database
MOBIHEALTH
ICST
DOI: 10.4108/eai.14-10-2015.2261640
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.
