Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

• Main Authors: Prinable, Joseph (Author), Jones, Peter (Author), Boland, David (Author), Thamrin, Cindy (Author), McEwan, Alistair (Author)
• Format: Book
• Published: JMIR Publications, 2020-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2291-5222; 10.2196/13737
• Keywords: Information technology; T58.5-58.64; Public aspects of medicine; RA1-1270
• Source: JMIR mHealth and uHealth, Vol 8, Iss 7, p e13737 (2020)

BackgroundThere 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. ObjectiveIn 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. MethodsA 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. ResultsOver 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). ConclusionsA 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.