ArticleJMIR mHealth and uHealth2020
Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates.Sensors (Basel, Switzerland) · 2022Article
- Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.Journal of asthma and allergy · 2022Review
- Measurement of respiratory rate using wearable devices and applications to COVID-19 detection.NPJ digital medicine · 2021Article
- Derivation of Respiratory Metrics in Health and Asthma.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.