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Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning

Authors :
Simon Stankoski
Ivana Kiprijanovska
Ifigeneia Mavridou
Charles Nduka
Hristijan Gjoreski
Martin Gjoreski
Source :
Sensors; Volume 22; Issue 6; Pages: 2079
Publication Year :
2022
Publisher :
Multidisciplinary Digital Publishing Institute, 2022.

Abstract

Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson’s correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device.

Details

Language :
English
ISSN :
14248220
Database :
OpenAIRE
Journal :
Sensors; Volume 22; Issue 6; Pages: 2079
Accession number :
edsair.doi.dedup.....d9bf69c581386b8499009e22d959ec58
Full Text :
https://doi.org/10.3390/s22062079