@Article{info:doi/10.2196/13737, author="Prinable, Joseph and Jones, Peter and Boland, David and Thamrin, Cindy and McEwan, Alistair", title="Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology", journal="JMIR Mhealth Uhealth", year="2020", month="Jul", day="31", volume="8", number="7", pages="e13737", keywords="photoplethysmogram; respiration; asthma monitoring; LSTM", abstract="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. ", issn="2291-5222", doi="10.2196/13737", url="http://mhealth.jmir.org/2020/7/e13737/", url="https://doi.org/10.2196/13737", url="http://www.ncbi.nlm.nih.gov/pubmed/32735229" }