Adaptive R-peak Detection Using Empirical Mode Decomposition

Christina Kozia, Randa Herzallah, David Lowe

    Research output: Unpublished contribution to conferenceUnpublished Conference Paperpeer-review


    Accurate QRS detection plays a pivotal role in the diagnosis of heart diseases and the estimation of heart rate variability and respiration rate. The investigation of R-peak detection is a continuing concern in computer-based ECG analysis because current methods are still inaccurate and miss heart beats. This paper presents a different algorithm to the state-of-the-art Empirical Mode Decomposition based algorithms for R-peak detection. Although our algorithm is based on Empirical Mode Decomposition, it uses an adaptive threshold over a sliding window combined with a gradient-based and refractory period
    checks to differentiate large Q peaks and reject false R peaks. The performance
    of the algorithm was tested on multiple databases including the MIT-BIH Arrhythmia database, Preterm Infant Cardio-Respiratory
    Signals database and the Capnobase dataset, achieving a detection rate over 99%. Our modified approach outperforms other published results using Hilbert or derivative-based methods on common databases.
    Original languageEnglish
    Number of pages12
    Publication statusPublished - 31 Jul 2018
    EventInternational Conference on Time Series and Forecasting, ITISE 2018 - Granada, Spain
    Duration: 19 Sept 201821 Sept 2018


    ConferenceInternational Conference on Time Series and Forecasting, ITISE 2018

    Bibliographical note

    © 2018 The Authors


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