Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment

TL;DR

This study develops machine learning algorithms to enable at-home respiratory monitoring, aiming to improve accessibility and patient autonomy in managing respiratory diseases outside clinical settings.

Contribution

It introduces a machine learning approach, especially using random forest, for at-home respiratory assessment based on data from healthy adults under various breathing conditions.

Findings

Random forest achieved highest accuracy in classifying breathing types.

Including breathing rate as a feature improved model performance.

Results support AI-driven systems for remote respiratory monitoring.

Abstract

Respiratory diseases impose a significant burden on global health, with current diagnostic and management practices primarily reliant on specialist clinical testing. This work aims to develop machine learning-based algorithms to facilitate at-home respiratory disease monitoring and assessment for patients undergoing continuous positive airway pressure (CPAP) therapy. Data were collected from 30 healthy adults, encompassing respiratory pressure, flow, and dynamic thoraco-abdominal circumferential measurements under three breathing conditions: normal, panting, and deep breathing. Various machine learning models, including the random forest classifier, logistic regression, and support vector machine (SVM), were trained to predict breathing types. The random forest classifier demonstrated the highest accuracy, particularly when incorporating breathing rate as a feature. These findings support the potential of AI-driven respiratory monitoring systems to transition respiratory assessments from clinical settings to home environments, enhancing accessibility and patient autonomy. Future work involves validating these models with larger, more diverse populations and exploring additional machine learning techniques.