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. 2020 Dec 12;20(24):7134.
doi: 10.3390/s20247134.

Derivation of Respiratory Metrics in Health and Asthma

Joseph Prinable  1   2 , Peter Jones  3 , David Boland  3 , Alistair McEwan  1 , Cindy Thamrin  2
Affiliations

Derivation of Respiratory Metrics in Health and Asthma

Joseph Prinable et al. Sensors (Basel). .

Abstract

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. To 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 were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) 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 LSTM vs. U-Net 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 absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(-2.31, 2.34) vs. -0.02(-2.19, 2.16), expiration time -0.19(-2.35, 1.98) vs. -0.24(-2.36, 1.89), and inter-breath intervals -0.19(-2.73, 2.35) vs. -0.25(2.76, 2.26). The inspiratory:expiratory ratios were -0.14(-1.43, 1.16) vs. -0.14(-1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(-2.51, 2.96) vs. 0.29(-2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate).

Conclusion: Both machine learning models show strong 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.

Keywords: LSTM; U-Net; asthma; machine learning; respiratory monitoring.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Illustrative examples of the photoplethysmographic signal showing: (A) no modulation or wander; (B) baseline wander; (C) amplitude modulation; (D) pulse width modulation potentially induced by breathing cycles.
Figure 2
Figure 2
Diagrammatical definitions of respiratory metrics derived from a volume trace.
Figure 3
Figure 3
Example window for 6, 8, 10, 12, and 14 breaths per minute is shown for Participant 1.
Figure 4
Figure 4
Percentage of valid windows exceeding corresponding Pearson correlation with the reference signal for the Long Short-Term Memory (LSTM) and U-shaped network (U-Net.)
Figure 5
Figure 5
Bland–Altman agreement expressed as percent differences for LSTM architecture.
Figure 6
Figure 6
Bland–Altman agreement expressed as percent differences for U-Net architecture.
See this image and copyright information in PMC

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