Adaptive R-peak Detection Using Empirical Mode Decomposition

Christina Kozia, Randa Herzallah, David Lowe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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
Title of host publicationInternational Conference on Time Series and Forecasting, ITISE 2018
Number of pages12
Publication statusAccepted/In press - 31 Jul 2018
EventInternational Conference on Time Series and Forecasting, ITISE 2018 - Granada, Spain
Duration: 19 Sep 201821 Sep 2018

Conference

ConferenceInternational Conference on Time Series and Forecasting, ITISE 2018
CountrySpain
CityGranada
Period19/09/1821/09/18

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Bibliographical note

© 2018 The Authors

Cite this

Kozia, C., Herzallah, R., & Lowe, D. (Accepted/In press). Adaptive R-peak Detection Using Empirical Mode Decomposition. In International Conference on Time Series and Forecasting, ITISE 2018