Adaptive R-peak Detection Using Empirical Mode Decomposition


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.

Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2018 The Authors
Event Title: International Conference on Time Series and Forecasting, ITISE 2018
Event Type: Other
Event Dates: 2018-09-19 - 2018-09-21
PURE Output Type: Paper
Published Date: 2018-07-31
Authors: Kozia, Christina
Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)
Lowe, David



Version: Accepted Version

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