TY  - JOUR
AU  - Prinable, Joseph
AU  - Jones, Peter
AU  - Boland, David
AU  - Thamrin, Cindy
AU  - McEwan, Alistair
PY  - 2020
DA  - 2020/7/31
TI  - Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology
JO  - JMIR Mhealth Uhealth
SP  - e13737
VL  - 8
IS  - 7
KW  - photoplethysmogram
KW  - respiration
KW  - asthma monitoring
KW  - LSTM
AB  - Background: There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. Objective: In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. Methods: A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. Results: Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). Conclusions: A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation. 
SN  - 2291-5222
UR  - http://mhealth.jmir.org/2020/7/e13737/
UR  - https://doi.org/10.2196/13737
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32735229
DO  - 10.2196/13737
ID  - info:doi/10.2196/13737
ER  -