Citation

De chazal P, Fox N, O'hare E, Heneghan C, Zaffaroni A, Boyle P, Smith S, O'connell C, Mcnicholas WT. J. Sleep Res. 2011; 20(2): 356-366.

Copyright

(Copyright © 2011, European Sleep Research Society, Publisher John Wiley and Sons)

DOI

10.1111/j.1365-2869.2010.00876.x

PMID

unavailable

Abstract

We studied a novel non‐contact biomotion sensor, which has been developed for identifying sleep/wake patterns in adult humans. The biomotion sensor uses ultra low‐power reflected radiofrequency waves to determine the movement of a subject during sleep. An automated classification algorithm has been developed to recognize sleep/wake states on a 30‐s epoch basis based on the measured movement signal. The sensor and software were evaluated against gold‐standard polysomnography on a database of 113 subjects [94 male, 19 female, age 53 ± 13 years, apnoea–hypopnea index (AHI) 22 ± 24] being assessed for sleep‐disordered breathing at a hospital‐based sleep laboratory. The overall per‐subject accuracy was 78%, with a Cohen’s kappa of 0.38. Lower accuracy was seen in a high AHI group (AHI >15, 63 subjects) than in a low AHI group (74.8% versus 81.3%); however, most of the change in accuracy can be explained by the lower sleep efficiency of the high AHI group. Averaged across subjects, the overall sleep sensitivity was 87.3% and the wake sensitivity was 50.1%. The automated algorithm slightly overestimated sleep efficiency (bias of +4.8%) and total sleep time (TST; bias of +19 min on an average TST of 288 min). We conclude that the non‐contact biomotion sensor can provide a valid means of measuring sleep–wake patterns in this patient population, and also allows direct visualization of respiratory movement signals.