Respiratory analysis during sleep using a chest-worn accelerometer: A machine learning approach. (September 2022) - Ryser, Franziska; Hanassab, Simon; Lambercy, Olivier; Werth, Esther; Gassert, Roger
Abstract: Objective: There is a great interest in observing breathing patterns during sleep, as sleep disturbances can be caused by respiratory irregularity and cessations. In this paper, we introduce the first steps to an accelerometer-based screening tool for respiratory rate estimation and a novel approach towards detecting breathing cessations such as apnea/hypopnea, by extending and combining established signal processing routines with machine learning. Methods: From a single chest-worn accelerometer, we estimate the respiratory rate based on the inhalation/exhalation movements of the chest and carry out a full overnight validation. On this basis, we build a set of features customized to detect irregular respiratory activity, including a novel feature: the respiratory peak variance (RPV). From thirteen healthy subjects, a classification model was trained, validated, and tested with over 98 h of PSG-labeled accelerometer data. Results: The algorithm estimated the respiratory rate with a mean difference of 1.8 breaths per minute compared to respiratory inductance plethysmography during overnight PSGs. The machine learning classifier detected respiratory cessations with a sensitivity and specificity of 76.05% and 70.05% respectively, with an overall accuracy of 70.95%. Conclusion: We successfully demonstrated the potential of a novel respiratory feature set in a preliminary application with young healthy volunteers for respiratory rate estimation and in identifying apnea/hypopnea events during overnight sleep. Significance: We present a simple and unobtrusive wearable system that can serve as a home screening tool for sleep-related breathing disorders. Graphical abstract: Highlights: Chest-worn accelerometer to monitor respiration during sleep. Validation of respiratory rate estimation in extensive overnight sleep. Novel feature set for accelerometer data customized to identify breathing. Machine learning model to discriminate between regular and disrupted breathing.