Microsleep episodes in the borderland between wakefulness and sleep

Abstract

Study objectives The wake-sleep transition zone represents a poorly defined borderland, containing, for example, microsleep episodes (MSEs), which are of potential relevance for diagnosis and may have consequences while driving. Yet, the scoring guidelines of the American Academy of Sleep Medicine (AASM) completely neglect it. We aimed to explore the borderland between wakefulness and sleep by developing the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring, focusing on MSEs visible in the electroencephalography (EEG), as opposed to purely behavior- or performance-defined MSEs. Methods Maintenance of Wakefulness Test (MWT) trials of 76 randomly selected patients were retrospectively scored according to both the AASM and the newly developed BERN scoring criteria. The visual scoring was compared with spectral analysis of the EEG. The quantitative EEG analysis enabled a reliable objectification of the visually scored MSEs. For less distinct episodes within the borderland, either ambiguous or no quantitative patterns were found. Results As expected, the latency to the first MSE was significantly shorter in comparison to the sleep latency, defined according to the AASM criteria. In certain cases, a large difference between the two latencies was observed and a substantial number of MSEs occurred between the first MSE and sleep. Series of MSEs were more frequent in patients with shorter sleep latencies, while isolated MSEs were more frequent in patients who did not reach sleep. Conclusion The BERN criteria extend the AASM criteria and represent a valuable tool for in-depth analysis of the wake-sleep transition zone, particularly important in the MWT.

Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment

“…In our study, a substantial number of MSEs were shorter than 3 s in both the MWT and the driving simulator. This might explain why our findings differ from those of Bougard et al (2018), while simultaneously underlining the need of broadly accepted MSE scoring criteria with a shorter minimum duration (Hertig-Godeschalk et al, 2019) than previously used (Harrison and Horne, 1996;Tirunahari et al, 2003;Bougard et al, 2018).…”

“…Similar to moderately sleepy patients in the MWT (Hertig-Godeschalk et al, 2019), the latency to the first MSE among severely sleepy participants (after sleep deprivation) was shorter than the sleep latency in both the MWT and the driving simulator. Both the latency to the first MSE and the sleep latency were approximately 14 min shorter in sleep-deprived participants compared to moderately sleepy patients (Hertig-Godeschalk et al, 2019), reflecting the extreme sleep pressure after a full night of sleep deprivation. Most participants did not fall asleep in the driving simulator (sleep latency was set to 60 min) which could have confounded the results but also shows the possible limitation of this measure.…”

“…Another MSE detection algorithm using deep learning based on raw data is currently under development [Malafeev et al, Automatic detection of microsleep episodes with deep learning (unpublished)]. Similar to moderately sleepy patients in the MWT (Hertig-Godeschalk et al, 2019), the latency to the first MSE among severely sleepy participants (after sleep deprivation) was shorter than the sleep latency in both the MWT and the driving simulator. Both the latency to the first MSE and the sleep latency were approximately 14 min shorter in sleep-deprived participants compared to moderately sleepy patients (Hertig-Godeschalk et al, 2019), reflecting the extreme sleep pressure after a full night of sleep deprivation.…”

“…The previously developed feature-based deep learning algorithm for automatic MSE detection (long short-term memory neural network) (Skorucak et al, 2019) is based on the BERN scoring criteria, which define MSEs as occipital EEG fragments similar to N1 but lasting 1-15 s while eyelids are ≥80% closed in the face videography (Hertig-Godeschalk et al, 2019). Further, MSEs are characterised by a slowing in the EEG with dominant theta activity.…”

“…Doghramji et al (1997) published normative data using 10-s epochs for scoring MWT recordings. Our recently published Bern continuous and high-resolution wake-sleep (BERN) scoring criteria are even more sensitive, defining continuously scored microsleep episodes (MSEs) with a minimum duration as short as 1 s (Hertig-Godeschalk et al, 2019). Even though the BERN scoring criteria enable a more standardised scoring of MSEs, the very time-consuming process of visual MSE scoring might discourage sleep medicine physicians to consider MSEs in clinical practice.…”