Respiratory events screening using consumer smartwatches

Abstract

Respiratory related events (RE) during nocturnal sleep disturb the natural physiological pattern of sleep. This events may include all types of apnea and hypopnea, respiratory-event-related arousals and snoring. The particular importance of breath analysis is currently associated with the COVID-19 pandemic. The proposed algorithm is a deep learning model with long short-term memory cells for RE detection for each 1 minute epoch during nocturnal sleep. Our approach provides the basis for a smartwatch based respiratory-related sleep pattern analysis (accuracy of epoch-by-epoch classification is greater than 80 %), can be applied for a potential risk of respiratory-related diseases screening (mean absolute error of AHI estimation is about 6.5 events/h on the test set, which includes participants with all types of apnea severity; two class screening accuracy (AHI threshold is 15 events/h) is greater than 90 %).