Purpose: Estimating the total sleep time in home recording devices is necessary to avoid underestimation of the indices reflecting sleep apnea and hypopnea syndrome severity, e.g., the apnea-hypopnea index (AHI). A new method to distinguish sleep from wake using jaw movement signal processing is assessed.
Methods: In this prospective study, jaw movement signal was recorded using the Somnolter (SMN) portable monitoring device synchronously with polysomnography (PSG) in consecutive patients complaining about a lack of recovery sleep. The automated sleep/wake scoring method is based on frequency and complexity analysis of the jaw movement signal. This computed scoring was compared with the PSG hypnogram, the two total sleep times (TST(PSG) and TST(SMN)) as well.
Results: The mean and standard deviation (in minutes) of TST(PSG) on the whole dataset (n = 124) were 407 ± 95.6, while these statistics were 394.2 ± 99.3 for TST(SMN). The Bland and Altman analysis of the difference between the two TST was 12.8 ± 57.3 min. The sensitivity and specificity (in percent) were 85.3 and 65.5 globally. The efficiency decreased slightly when AHI lies between 15 and 30, but remained similar for lower or greater AHI. In the 24 patients with insomnia/depression diagnosis, a mean difference in TST of -3.3 min, a standard deviation of 58.2 min, a sensitivity of 86.3%, and a specificity of 66.2% were found.
Conclusions: Mandible movement recording and its dedicated signal processing for sleep/wake recognition improve sleep disorder index accuracy by assessing the total sleep time. Such a feature is welcome in home screening methods.
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