Objective dyspnea measurement in real time using a forehead wearable and artificial intelligence.

Abstract

Dyspnea is a complex symptom measured using subjective patient-reported ratings. Continuous, automated dyspnea measurements are needed, especially in critical care and trauma settings with impaired patient communication. We prospectively enrolled 54 pulmonary rehabilitation subjects. Participants completed two treadmill walking trials, during which dyspnea measurements were collected at one-minute intervals automatically using physiologic sensors and patient-reported ratings of perceived breathlessness and exertion (RPB and RPE). The sensor data were used to train machine learning models using either 19 or 7 features to generate an objective dyspnea score (ODS). Classification performance was assessed on a held-out test set, compared against patient-reported RPB and RPE. The model trained on 7 features performed best, resulting in a correlation between predicted and actual scores (percent accuracy) of 0.84 (78.7%) for RPE and 0.86 (83.6%) for RPB. Our system incorporating ODS accurately predicts patients' subjective dyspnea scores and is promising for automated, real-time dyspnea measurement.