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Adaptive notch- filtration to effectively recover photoplethysmographic signals during physical activity

Physical activity can severely influence the quality of photoplethysmographic (PPG) signals due to motion artefacts (MA). This study aims to extract heart rate (HR) and respiration rate (RR) values from raw PPG signals captured from a multi-wavelength illumination optoelectronic patch sensor (mOEPS) during physical activity of different intensities, and to do this in an effective manner. The proposed method, combined with a 3-axis accelerometer as a motion reference, was developed for the extraction of the desired PPG signals. The overall algorithm comprises three parts: 1) the adaptive moving average filter, 2) the adaptive notch filter, and 3) the physiological parameter extraction. 24 healthy subjects completed four stages of exercise of increasing intensity, first on a cycle ergometer and later on a treadmill. The recovered PPG signals for the calculation of HR and RR were comparable to the measurements from commercial devices, with an average absolute error for HR of <1.0 beats/min for the IEEE-SPC dataset, and 1.3 beats/min for HR, and 2.8 breaths/min for RR, from the in-house dataset obtained at Loughborough University. The method used is found to have good robustness and low complexity, making it suitable for application in real-time physiological monitoring.

History

School

  • Mechanical, Electrical and Manufacturing Engineering
  • Sport, Exercise and Health Sciences

Published in

Biomedical Signal Processing and Control

Volume

72

Issue

Part A

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Biomedical Signal Processing and Control and the definitive published version is available at https://doi.org/10.1016/j.bspc.2021.103303.

Acceptance date

23/10/2021

Publication date

2021-11-09

Copyright date

2021

ISSN

1746-8094

Language

en

Depositor

Dr Sijung Hu. Deposit date: 8 November 2021

Article number

103303