Research Article
Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database
@ARTICLE{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}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={2}, number={8}, publisher={ACM}, journal_a={PHAT}, 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
PHAT
EAI
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.
Copyright © 2015 V. Tsoutsouras et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
