phat 16(8): e5

Research Article

Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database

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  • @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
Vasileios Tsoutsouras1,*, Dimitra Azariadi1, Sotirios Xydis1, Dimitrios Soudris1
  • 1: N.T.U.A.
*Contact email: billtsou@microlab.ntua.gr

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.