Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology
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-ter...
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| Main Authors: | , , , , |
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| Format: | Book |
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JMIR Publications,
2020-07-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | 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. |
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| Item Description: | 2291-5222 10.2196/13737 |