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We aimed at developing a robust and reliable methodology for the automatic detection of sleep-to-wake transitions (S-WTs) based on heart-rate variability (HRV) signals. Therefore, we initially reviewed several aspects related to the characteristic features of HRV signals and then selected a tailored set of acoustic features that best describe HRV dynamics. We designed two different experimental sessions to provide robustness for the detection of S-WTs and for the analysis of circadian rhythmicity: (i) participants were monitored in a home environment and (ii) participants were monitored at the hospital. The performance of the methods for S-WTs detection was explored using two different standard scoring criteria: the traditional sleep-quality and respiration-derived scores and a new objective measure based on automated analysis of EEG-COG signals. We compared the performance of these methodologies with a manual scoring of S-WTs. We first tested the capability of the methods to properly identify S-WTs and we then examined whether the use of acoustic features differed depending on the above-mentioned sleep-quality and respiration-based scoring criteria. The proposed methodology was able to detect S-WTs in the majority of cases in both home and hospital environments (above 95%). The new methodology was 0b46394aab
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