Derivation of Respiratory Metrics in Health and Asthma

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.

The 2023 wearable photoplethysmography roadmap

“…Signal quality becomes increasingly important when measuring complex respiration parameters such as tidal volume and inspiration/expiration time. Several studies have found that the PPG can be used to monitor respiratory parameters and identify disordered breathing patterns associated with chronic obstructive pulmonary disease (COPD) (Davies et al 2022), interstitial lung disease (Yamazaki and Fujimoto 2021), sleep apnea (Kang et al 2020) and asthma (Prinable et al 2020). However, these studies were limited to controlled, atrest settings with higher-resolution devices.…”

“…LSTMs have also been used to measure RR (Baker et al 2021). Other models, such as random forest and U-Net, have also been considered (Davies et al 2022) (Prinable et al 2020).…”

Oximetría de pulso en enfermedades respiratorias

“…La desaturación nocturna corresponde a una SaO 2 < 90 % durante un 30 % o más del tiempo de monitoreo. La apnea del sueño se caracteriza por cambios cíclicos en la SaO 2 considerán- Adicionalmente el oxímetro de pulso cuenta con un fotopletismógrafo, dispositivo que mide la absorbancia de la O 2 Hb a luz verde (500 -600 nm) e infrarroja para cuantificar los cambios en el flujo sanguíneo durante la sístole y la diástole y para determinar el ritmo cardiaco y el ciclo respiratorio (21,29).…”