Machine-learning based feature selection for a non-invasive breathing change detection

Abstract

Background: Chronic Obstructive Pulmonary Disease (COPD) is one of the top 10 causes of death worldwide, representing a major public health problem. Researchers have been looking for new technologies and methods for patient monitoring with the intention of an early identification of acute exacerbation events. Many of these works have been focusing in breathing rate variation, while achieving unsatisfactory sensitivity and/or specificity. This study aims to identify breathing features that better describe respiratory pattern changes in a short-term adjustment of the load-capacity-drive balance, using exercising data.

Results: Under any tested circumstances, breathing rate alone leads to poor capability of classifying rest and effort periods. The best performances were achieved when using Fourier coefficients or when combining breathing rate with the signal amplitude and/or ARIMA coefficients.

Conclusions: Breathing rate alone is a quite poor feature in terms of prediction of breathing change and the addition of any of the other proposed features improves the classification power. Thus, the combination of features may be considered for enhancing exacerbation prediction methods based in the breathing signal.

Trial registration: ClinicalTrials NCT03753386. Registered 27 November 2018, https://ichgcp.net/clinical-trials-registry/NCT03753386.

Keywords: Chronic obstructive pulmonary disease (COPD); Classification; Novelty detection; Respiratory pattern; Telemonitoring.

Conflict of interest statement

JAP is employed by SRETT. JAP, TS, JG-B and EB are inventors of the patent EP20315396.0 (pending application), in which a respiration monitoring system combining breathing rate and signal amplitude measures is covered.

© 2021. The Author(s).

Figures

Fig. 1

Example of pressure signal recorded with TeleOxⓇ. Window of 45 seconds of nasal pressure signal from a healthy subject recording

Fig. 2

Full pressure signal TeleOxⓇ recordings for two healthy subjects. Dark gray areas correspond to the 3 minutes of exercising while the light gray areas correspond to breathing while drinking, coughing, speaking and oral breathing. Subjects were at rest in other areas

Fig. 3

Pressure signal and oxygen flow for 8-hours recording from COPD patient. Gray areas correspond to estimated exercise times

Fig. 4

Extracted features example from a healthy subject recording. a Raw pressure signal, b breathing rate, signal amplitude and ARIMA coefficients and c Fourier transform. In a and b, dark gray areas correspond to the 3 minutes of exercising while the light gray areas correspond to breathing while drinking, coughing, speaking and mouth breathing

Fig. 5

Extracted features example from a COPD patient recording. a Raw pressure signal, b breathing rate, signal amplitude and ARIMA coefficients and c. Fourier transform. In a and b, dark gray areas correspond to estimated exercise times

Fig. 6

ROC curves for the detection of exercise periods in the supervised context using combinations of the proposed features. a Healthy subjects and b Patients with COPD

Fig. 7

ROC curves for the detection of exercise periods in the one-class context using combinations of the proposed features a Healthy subjects and b Patients with COPD

Fig. 8

Example of Mahalanobis distances from reference points. In the one-class context, distances between each new measure x and the all reference points are given by the Mahalanobis distance, considering the reference’s mean and variance-covariance matrix