Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment
This work addresses the need for accessible at-home respiratory assessments for patients on CPAP therapy, though it is incremental as it applies existing methods to new data.
The paper tackled the problem of at-home respiratory disease monitoring by developing machine learning algorithms to predict breathing types using data from 30 healthy adults, with the random forest classifier achieving the highest accuracy when including breathing rate as a feature.
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.