A patient's breathing pattern reflects their work of breathing, and it can be monitored in patients to assess changes in their respiratory condition. Although current diagnostic and management practices primarily rely on specialist clinical testing, there is a need to monitor patients in field and combat hospitals or in transit from the point of injury. This work aims to develop machine learning-based algorithms to facilitate remote respiratory disease monitoring and assessment for patients undergoing continuous positive airway pressure 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. These data were sourced from the publicly available PhysioNet, with associated ethical approval and informed consent. Various machine learning models, including the random forest classifier, logistic regression, and support vector machine, were trained to predict the correct breathing condition. Leave-one-out and k-fold cross-validation were performed. The random forest classifier demonstrated the highest accuracy, which was 88% after incorporating breathing rate as a feature. These findings support the potential of AI-driven respiratory monitoring systems to transition respiratory assessments from traditional clinical settings to remote monitoring, enhancing accessibility and patient autonomy. Future work involves validating these models in a large prospective study involving individuals who are actively deployed, while exploring additional machine learning techniques. Determining the correct breathing pattern relates to the patient's work of breathing, a marker of clinical deterioration of key importance for deployed soldiers. Early and accurate detection of the patient's respiratory condition can potentially improve outcomes in resource-constrained settings, particularly field hospitals and injured soldiers in transit from the point of injury. This work supports the potential of AI-driven respiratory monitoring to enhance remote respiratory monitoring, allowing for timely intervention and better long-term care.
Orangi-Fard et al. (Mon,) studied this question.