Imperial College London
Portrait Picture

ProfessorDaniloMandic

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Machine Intelligence
 
 
 
//

Contact

 

+44 (0)20 7594 6271d.mandic Website

 
 
//

Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
//

Location

 

813Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Davies:2023:10.1109/tcds.2022.3196841,
author = {Davies, HJ and Williams, I and Hammour, G and Yarici, M and Stacey, MJ and Seemungal, BM and Mandic, DP},
doi = {10.1109/tcds.2022.3196841},
journal = {IEEE Transactions on Cognitive and Developmental Systems},
pages = {950--958},
title = {In-ear SpO2 for classification of cognitive workload},
url = {http://dx.doi.org/10.1109/tcds.2022.3196841},
volume = {15},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The brain is the most metabolically active organ in the body, which increases its metabolic activity, and thus oxygen consumption, with increasing cognitive demand. This motivates us to question whether increased cognitive workload may be measurable through changes in blood oxygen saturation. To this end, we explore the feasibility of cognitive workload tracking based on in-ear SpO2 measurements, which are known to be both robust and exhibit minimal delay. We consider cognitive workload assessment based on an N-back task with randomised order. It is shown that the 2-back and 3-back tasks (high cognitive workload) yield either the lowest median absolute SpO2 or largest median decrease in SpO2 in all of the subjects, indicating a measurable and statistically significant decrease in blood oxygen in response to increased cognitive workload. This makes it possible to classify the four N-back task categories, over 5 second epochs, with a mean accuracy of 90.6%, using features derived from in-ear pulse oximetry, including SpO2, pulse rate and respiration rate. These findings suggest that in-ear SpO2 measurements provide sufficient information for the reliable classification of cognitive workload over short time windows, which promises a new avenue for real time cognitive workload tracking.
AU  - Davies,HJ
AU  - Williams,I
AU  - Hammour,G
AU  - Yarici,M
AU  - Stacey,MJ
AU  - Seemungal,BM
AU  - Mandic,DP
DO  - 10.1109/tcds.2022.3196841
EP  - 958
PY  - 2023///
SN  - 2379-8920
SP  - 950
TI  - In-ear SpO2 for classification of cognitive workload
T2  - IEEE Transactions on Cognitive and Developmental Systems
UR  - http://dx.doi.org/10.1109/tcds.2022.3196841
UR  - https://ieeexplore.ieee.org/document/9851405
UR  - http://hdl.handle.net/10044/1/99382
VL  - 15
ER  -
Download