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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2020-07-01
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| Series: | JMIR mHealth and uHealth |
| Online Access: | http://mhealth.jmir.org/2020/7/e13737/ |
| _version_ | 1818919447735304192 |
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| author | Prinable, Joseph Jones, Peter Boland, David Thamrin, Cindy McEwan, Alistair |
| author_facet | Prinable, Joseph Jones, Peter Boland, David Thamrin, Cindy McEwan, Alistair |
| author_sort | Prinable, Joseph |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T01:06:00Z |
| format | Article |
| id | doaj.art-a0ffde27468644ea97d19f35a3ee1b05 |
| institution | Directory Open Access Journal |
| issn | 2291-5222 |
| language | English |
| last_indexed | 2024-12-20T01:06:00Z |
| publishDate | 2020-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR mHealth and uHealth |
| spelling | doaj.art-a0ffde27468644ea97d19f35a3ee1b052022-12-21T19:58:51ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222020-07-0187e1373710.2196/13737Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning MethodologyPrinable, JosephJones, PeterBoland, DavidThamrin, CindyMcEwan, AlistairBackgroundThere 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.http://mhealth.jmir.org/2020/7/e13737/ |
| spellingShingle | Prinable, Joseph Jones, Peter Boland, David Thamrin, Cindy McEwan, Alistair Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology JMIR mHealth and uHealth |
| title | Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology |
| title_full | Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology |
| title_fullStr | Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology |
| title_full_unstemmed | Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology |
| title_short | Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology |
| title_sort | derivation of breathing metrics from a photoplethysmogram at rest machine learning methodology |
| url | http://mhealth.jmir.org/2020/7/e13737/ |
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