Imperial College London
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ProfessorPhilipMolyneaux

Faculty of Medicine, National Heart & Lung Institute

Professor of Interstitial Lung Disease
 
 
 
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Contact

 

p.molyneaux

 
 
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Location

 

Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Davies:2022:10.1109/TBME.2022.3145688,
author = {Davies, HJ and Bachtiger, P and Williams, I and Molyneaux, PL and Peters, NS and Mandic, D},
doi = {10.1109/TBME.2022.3145688},
journal = {IEEE Transactions on Biomedical Engineering},
title = {Wearable in-ear PPG: detailed respiratory variations enable classification of COPD},
url = {http://dx.doi.org/10.1109/TBME.2022.3145688},
volume = {69},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.
AU  - Davies,HJ
AU  - Bachtiger,P
AU  - Williams,I
AU  - Molyneaux,PL
AU  - Peters,NS
AU  - Mandic,D
DO  - 10.1109/TBME.2022.3145688
PY  - 2022///
SN  - 0018-9294
TI  - Wearable in-ear PPG: detailed respiratory variations enable classification of COPD
T2  - IEEE Transactions on Biomedical Engineering
UR  - http://dx.doi.org/10.1109/TBME.2022.3145688
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35077352
UR  - https://ieeexplore.ieee.org/document/9693297
UR  - http://hdl.handle.net/10044/1/96220
VL  - 69
ER  -
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