Kozia, Christina, Herzallah, Randa and Lowe, David (2018). Adaptive R-peak Detection Using Empirical Mode Decomposition. IN: International Conference on Time Series and Forecasting, ITISE 2018. 2018-09-19 - 2018-09-21.
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.
Divisions: | College of Engineering & Physical Sciences > Systems analytics research institute (SARI) |
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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 |
Last Modified: | 30 Sep 2024 08:48 |
Date Deposited: | 10 Dec 2018 10:52 | PURE Output Type: | Paper |
Published Date: | 2018-07-31 |
Authors: |
Kozia, Christina
Herzallah, Randa ( 0000-0001-9128-6814) Lowe, David |