Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Nov 12, 2020)
Date Submitted: Oct 21, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Derivation of Respiratory Metrics in Health and Asthma; Machine Learning Methodology
ABSTRACT
Background:
The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies.
Objective:
Examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring.
Methods:
Pulse oximetry data was collected from 11 healthy and 11 asthma subjects who breathed at a range of controlled respiratory rates. UNET and Long Short-Term memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference.
Results:
The UNET vs LSTM model provided breathing metrics which were strongly correlated with those from the reference signal (all p<0.001, except for inspiratory:expiratory ratio). The following relative mean bias(95% confidence interval) were observed: inspiration time 1.89(-52.95, 56.74)% vs 1.30(-52.15, 54.74)%, expiration time -3.70(-55.21, 47.80)% vs -4.97(-56.84, 46.89)%, inspiratory:expiratory ratio -4.65(-87.18, 77.88)% vs -5.30(-87.07, 76.47)%, inter-breath intervals -2.39(-32.76, 27.97)% vs -3.16(-33.69, 27.36)%, and respiratory rate 2.99(-27.04 to 33.02)% vs 3.69(-27.17 to 34.56)%.
Conclusions:
Both machine learning models show strongly correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g. by increasing the size of the training dataset at the lower breathing rates. Clinical Trial: Sydney Local Health District Human Research Ethics Committee (#LNR\16\HAWKE99 ethics approval).
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