Development and validation of an algorithm for the study of sleep using a biometric shirt in young healthy adults

Summary

Portable polysomnography is often too complex and encumbering for recording sleep at home. We recorded sleep using a biometric shirt (electrocardiogram sensors, respiratory inductance plethysmography bands and an accelerometer) in 21 healthy young adults recorded in a sleep laboratory for two consecutive nights, together with standard polysomnography. Polysomnographic recordings were scored using standard methods. An algorithm was developed to classify the biometric shirt recordings into rapid eye movement sleep, non-rapid eye movement sleep and wake. The algorithm was based on breathing rate and heart rate variability, body movement, and included a correction for sleep onset and offset. The overall mean percentage of agreement between the two sets of recordings was 77.4%; when non-rapid eye movement and rapid eye movement sleep epochs were grouped together, it increased to 90.8%. The overall kappa coefficient was 0.53. Five of the seven sleep variables were significantly correlated. The findings of this pilot study indicate that this simple portable system could be used to estimate the general sleep pattern of young healthy adults.

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