# Shedding light on microsleep episodes for comprehensive sleepiness assessment: a narrative review

Aurélie Davin, Renaud Tamisier, Lucie Barateau, Laure Peter-Derex, Yves

Dauvilliers, Jean-Louis Pépin, Sébastien Baillieul

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Aurélie Davin, Renaud Tamisier, Lucie Barateau, Laure Peter-Derex, Yves Dauvilliers, et al.. Shedding light on microsleep episodes for comprehensive sleepiness assessment: a narrative review. Sleep Medicine Reviews, 2025, 82, pp.102113. 10.1016/j.smrv.2025.102113. hal-05138782

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# Shedding light on microsleep episodes for comprehensive sleepiness assessment: a narrative review

Aurélie Davin<sup>a,†</sup>, Renaud Tamisier<sup>a</sup>, Lucie Barateau<sup>b,c</sup>, Laure Peter-Derex<sup>d,e</sup>, Yves Dauvilliers<sup>b,c</sup>, Jean-Louis Pépin<sup>a,1</sup>, Sébastien Baillieul<sup>a,1,* †</sup>

## ARTICLE INFO

Handling Editor: Monica Andersen

Keywords:

Microsleep

Sleepiness

Performances

Accident

Traffic

Countermeasures

## ABSTRACT

Excessive daytime sleepiness is a consistent and common symptom in sleep medicine. It represents a major public health problem due to its association with significant impairments in quality of life, work productivity, and driving ability. Objective clinical tests for assessing excessive daytime sleepiness follow the universal American academy of sleep medicine scoring classification, which overlooks short intrusions of sleep during wake states, known as microsleep episodes. This narrative review provides a comprehensive summary of the existing literature concerning microsleep episodes, highlighting their significance and potential additional value in the context of sleepiness assessment. Moreover, due to the related attention lapses they cause, microsleep episodes may have significant implications for cognitive performance and road traffic accidents. These are discussed along with potential effective countermeasures. This review concludes by proposing an innovative framework for enriching excessive daytime sleepiness evaluation by integrating the assessment of microsleep episodes into routine clinical tests. Such an approach promises to provide valuable insights into the dynamics of sleepiness and could significantly enhance excessive daytime sleepiness assessment and management through personalized medicine.

## Glossary of terms

**Epworth sleepiness scale:** Subjective daytime sleepiness evaluation tool; individuals rate the probability of falling asleep 'in recent times', in eight common situations (0: never; 1: low probability; 2: moderate probability; 3: high probability; maximum score of 24). A score ≥10 indicates excessive daytime sleepiness, and a score ≥16 indicates severe condition, as established in the original work by Johns (reference [10]).

**Excessive daytime sleepiness:** This is defined as an inability to stay awake or alert during the major waking hours, often causing periods of irrepressible need for sleep or unintended lapses into drowsiness or sleep.

**Hypersomnolence:** Hypersomnolence is a condition

characterized by excessive daytime sleepiness, an excessive quantity of sleep (e.g. hypersomnia, defined as sleep time of ≥10 h during a 24-h period) and impaired vigilance after waking up, so-called sleep inertia (reference [8]).

**Karolinska sleepiness scale:** Subjective sleepiness evaluation tool where individuals rate their psycho-physical state 'in the last 10 min' on a scale ranging from 1 (very awake) to 9 (very sleepy fighting against sleep). A score ≥7 indicates sleepiness.

**Local sleep:** Local sleep refers to transient and regional neurophysiological states showing a simultaneous co-occurrence of 1) wake and sleep, 2) different sleep stages, or 3) different sleep depths between brain regions.

<footnote><sup>a</sup> Univ. Grenoble Alpes, HP2 Laboratory, Inserm U-1300, CHU Grenoble Alpes, 38043, Grenoble, France</footnote>

<footnote><sup>b</sup> Sleep/Wake Disorders Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, 34000, Montpellier, France</footnote>

<footnote><sup>c</sup> Institute Neurosciences of Montpellier (INM), Inserm U-1298, 34000, Montpellier, France</footnote>

<footnote><sup>d</sup> Lyon Neuroscience Research Center, Inserm U-1028, CNRS, UMR 5292, 69500, Lyon, France</footnote>

<footnote><sup>e</sup> Center for Sleep Medicine, Croix-Rousse Hospital, Hospices Civils de Lyon, Lyon 1 University, 69000, Lyon, France</footnote>

<footnote>* Laboratoire du sommeil, CHU Grenoble Alpes, CS10217, 38043, Grenoble Cedex 9, France
E-mail address: sbaillieul@chu-grenoble.fr (S. Baillieul).</footnote>

<footnote n="1">¹ Co-last authors.</footnote>
**Microsleep:** A microsleep episode is defined as a brief sleep intrusion during wakefulness, often accompanied by eye closure and reduced responsiveness. Historically, microsleep episodes have been described as lasting between 3 and 15 s, mirroring the duration criteria for micro-arousals. In 2020, the BERN criteria extended the definition of microsleep episodes as lasting from 1 to 15 s (reference [38]). It is important to note that there is currently no universally accepted definition of microsleep episodes.

**Sleepiness:** Sleepiness is a ubiquitous phenomenon characterized by a compelling need for sleep, influenced by the body internal clock (circadian rhythm) and accumulated sleep pressure. While sleepiness occurs naturally over any given 24-h period, inappropriate or excessive sleepiness may indicate an underlying medical condition.

## Abbreviations

**AASM** American Academy of Sleep Medicine

**EDS** Excessive daytime sleepiness

**EEG** Electroencephalogram

**EMG** Electromyogram

**EOG** Electrooculogram

**ESS** Epworth sleepiness scale

**KSS** Karolinska sleepiness scale

**MS** Microsleep

**MSLT** Multiple sleep latency test

**MWT** Maintenance of wakefulness test

**OSLER** Oxford sleep resistance test

**PERCLOS-80** Percentage of time when eyes are at least 80% closed

**PVT** Psychomotor vigilance test

**REM** Rapid eye movement

**SEM** Slow eye movement

# 1. Introduction

Excessive daytime sleepiness (EDS) is one of the most frequent complaints in sleep medicine, significantly impacting quality of life, work performance, and driving ability [1-4]. The International Classification of Sleep Disorders, Third Edition, text revision, defines EDS as the inability to stay awake and alert during major waking periods [5] resulting from heterogeneous underlying causes including sleep deprivation and sleep disorders [6,7]. EDS encompasses a wide spectrum of clinical manifestations, ranging from a continuous state of persistent sleepiness to sudden sleep attacks or unintended naps, with varying degrees of severity, that need to be assessed [8].

Sleepiness assessment tools can be categorized into self-reported and objective measures [9]. The Epworth sleepiness scale (ESS) is clinically effective for evaluating daytime sleepiness [10], but its reliance on passive situations (which might be outdated for younger generations) could reduce its ability to determine real-world risks. The Karolinska sleepiness scale (KSS) correlates well with objective behavioral and electrophysiological markers of sleepiness [11,12], but only captures a specific moment in time and is rarely used in clinical practice (see glossary). Disease-specific scales have been validated to better capture and assess the severity and impact of sleepiness [13,14]. While practical, these subjective measures rely on the patient's perception (which may be susceptible to introspective bias [15] or altered by the impact of sleepiness on cognitive functioning [16]), making objective measures essential in the clinical assessment of sleepiness. The main objective tools, some of which use behavioral measurements, are the psychomotor

vigilance test (PVT), the Oxford sleep resistance test (OSLER), and driving simulators or real-road driving assessments, which mainly evaluate reaction times (see Table 1). Other objective evaluations of sleepiness involve neurophysiological measurements (typically a simultaneous recording of the electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG)) during standardized neurophysiological clinical tests [17]. Of these, the multiple sleep latency test (MSLT) is considered as the gold standard for identifying EDS because it measures an individual's propensity to fall asleep under standardized conditions. In contrast, the maintenance of wakefulness test (MWT) is used to assess fitness to drive and the effectiveness of therapeutic interventions, including pharmacological treatments (e.g. wake-promoting agents) by evaluating the ability to remain awake in a low-stimulation environment. In these neurophysiological tests, specific sleep onset latency thresholds for MSLT [18] and MWT [19,20] are used to evaluate the severity of sleepiness or alerted wakefulness (see Table 1). Sleep onset is defined using the visual scoring rules established by the American Academy Sleep Medicine (AASM), which use standardized electrophysiological criteria to classify 30-s epochs of polysomnography recordings into wakefulness or sleep stages (non-rapid eye movement (REM) sleep stages N1 to N3, and REM sleep) based on the predominant activity (>50% of the epoch). Historically, EEG recordings were acquired on paper at a standard speed of 10 mm/s, with each 30-s epoch corresponding to a single page of EEG data, a method that shaped early sleep scoring practices and continues to this day, even in the digital era. However, sleep/wake states are less homogeneous and stable than these scoring rules and the traditional view of “all-or-nothing” phenomena suggest.

Several studies have provided compelling evidence that wakefulness and sleep are subject to local regulation [21-23]. For instance, during non-REM sleep, different brain regions may show varying depths of sleep [24,25], with local arousals and asynchronous awakenings [26-28]. Conversely, certain brain areas can fall asleep while others remain awake [29-32], supported by the observation of local sleep-like slow waves during wakefulness, leading to the concept of “local sleep” [33-36]. The dynamic properties of sleep onset, the features of which evolve over timescales shorter than the conventional 30-s epoch, can give rise to brief (i.e. <15 s) and local fluctuations at the boundary between wakefulness and sleep that are excluded from standard analyses [37]. Microsleep (MS) episodes are a prime example of this phenomenon. They are identified as short—by definition lasting <15 s—intrusions of sleep during wakefulness [38], showing EEG features comparable to sleep onset (i.e. shift from fast wake-related brain activity to slower theta waves). This cutoff has important clinical implications because the identification of EDS and the evaluation of fitness to drive rely on measuring sleep onset latency in clinical tests. Like arousals during sleep, the spatial characteristics of MS episodes are not accounted for in current scoring criteria. They are considered to be local phenomena—by definition occurring in at least one occipital EEG channel [38]—primarily due to the limited spatial resolution of standard EEG setups. Additionally, variations in the definitions of EDS, along with limitations in clinical assessments, contribute to it being underestimated and affect the validity of clinical and epidemiological studies addressing EDS [39]. Up-to-date research on sleepiness evaluation is crucial to improve the accuracy of EDS assessment and management.

The aims of this review are to: 1) dissect the concept of MS episodes and assess their relevance in the field of sleepiness and various sleepiness phenotypes; 2) delineate the cognitive and behavioral correlates of MS episodes for daily activities; 3) identify effective countermeasures, or at least avenues, to reduce MS episodes; and 4) propose a research agenda aimed at determining consensual scoring rules for reproducible identification of MS episodes, ultimately leading to improved EDS screening and phenotyping.
## 2. Literature search and data synthesis

Relevant published literature was identified using a PubMed search without date restriction (from 1975 to July 2024) using the following combination of terms: 'microsleep' OR 'microsleep episodes' OR 'micro-sleep'. Additional relevant terms, including 'sleepiness', 'performance', and 'countermeasures' were used to refine the results (see Table S1). The term "microsleep" is increasingly used in the literature (Fig. S1). Only articles in English were considered. The reference lists of all relevant articles were checked to identify relevant publications/sources. Retrieved references were individually checked for appropriateness. The final list of references was approved by all authors.

## 3. Identification of microsleep episodes: primary and surrogate criteria

Several studies have advanced our understanding of the electro-physiological and physiological changes associated with MS episodes in both the temporal and spatial domains. These findings have enhanced our overall understanding of MS episodes and are discussed in this section.

<table><caption>**Table 1**
Main objective sleepiness assessments.</caption><tr><td>► Multiple sleep latency test: This neurophysiological test facilitates the objective diagnosis of excessive daytime sleepiness (in the context of central disorders of hypersomnolence) by assessing the ability or tendency to fall asleep in standardized conditions. Sleep onset latency is defined as the time from the start of the trial until the first epoch of any sleep stage (N1, N2, N3 or REM).</td><td>► Maintenance wakefulness test: This neurophysiological test objectively assesses the capacity to maintain wakefulness in a low-stimulation environment. Sleep onset latency is defined as the time from the start of the trial until the first epoch of any sleep stage (N1, N2, N3 or REM).</td></tr><tr><td>• Instruction: "Please lie quietly, in a comfortable position, keep your eyes closed, and allow yourself to fall asleep."</td><td>• Instruction: "Please sit still and remain awake for as long as possible."</td></tr><tr><td>• Iteration: 5 trials, conducted every 2 h;</td><td>• Iteration: 4 trials, conducted every 2 h.</td></tr><tr><td>• Duration: Each trial ends after 20 min if no sleep occurs; if sleep does occur then the trial continues for an additional 15 min, regardless of the amount of intervening sleep or wake.</td><td>• Duration: Each trial ends after 40 min if no sleep occurs; if sleep does occur then the trial ends after three consecutive 30-s epochs of N1 sleep or one epoch of any other sleep stage (N2, N3 or REM).</td></tr><tr><td>• Pathological threshold: Mean sleep onset latency of &lt;8 min. The presence of REM sleep within 15 min of sleep onset (SOREM) is a key diagnostic criterion for disorders such as narcolepsy (type I and type II) and idiopathic hypersomnia.</td><td>• Proposed pathological thresholds: Definitive abnormal thresholds for mean sleep latency remain undefined. Literature suggests a normal mean sleep latency of 30.4 ± 11.2 min. Doghramji et al. [16] proposed a lower normal threshold of 12.9 min for sleep onset and 19.4 min for sustained sleep (three consecutive N1 epochs or one epoch of any other stage). Philip et al. [17] recommend a 33-min threshold to minimize accident.</td></tr><tr><td>► Psychomotor vigilance test: This widely used neurobehavioral assessment tool is designed to measure an individual's alertness by recording response times to visual (or auditory) stimuli.</td><td>► Oxford sleep resistance test: this simple, non-invasive neurobehavioral test is used to assess an individual's alertness by recording response times. "Sleep" onset is defined as the omission of seven consecutive stimuli (i.e. 21 s without a response). This test does not directly assess sleep but serves as a behavioral test that indirectly reflects sleepiness.</td></tr><tr><td>• Instruction: The participant is asked to press a button as quickly as possible when the stimulus appears. The stimulus is presented at random inter-stimulus intervals, typically ranging from 2 to 10 s, in the standard 10-min psychomotor vigilance test (the total duration of the test may vary; a 5-min psychomotor vigilance test is often used when tests are repeated throughout the day).</td><td>• Instruction: The participant is asked to press a button in response to the lighting of a diode programmed to light up for 1 s every 3 s.</td></tr><tr><td>• Responses exceeding a set threshold (e.g. 500 ms) or misses are considered lapses, indicating periods of inattention.</td><td>• Duration: 40 min.</td></tr><tr><td></td><td>• Iteration: 4-5 times, every 2 h.</td></tr></table>

Similar to neurophysiological investigations of sleep and sleep onset, the reference tool for studying MS episodes involves the simultaneous recording of EEG, EOG and EMG. Since the 1970s, MS episodes have been studied in both humans and animals [40,41]. In these investigations, the minimum duration of MS episodes has been defined as 3 s and these episodes are characterized by a shift from attenuated alpha activity to theta activity, or more vaguely described as "a short-lived burst of typical stage 1 sleep", similar to the definition of micro-arousals. These criteria were initially adopted and used in most of the studies referenced in this review. However, this approach can lead to the exclusion of a significant proportion of MS episodes due to the use of an overly restrictive timescale or unclear criteria. A 2020 study highlighted that around 40 % of MS episodes last from 1 to 3 s [38], underscoring the need for more precise criteria to accurately identify these micro-events. To address these limitations, the authors of that paper developed the BERN criteria, an extension of the AASM guidelines, which provide clear principles for detecting MS episodes based on electrophysiological features [38]. Using a standard EEG setup (O1-M2, O2-M1, C3-M2, C4-M1, CZ-M1, F3-M2, F4-M1) and additional sensors such as EOG and EMG channels and video recording, the BERN criteria define MS episodes based on specific EEG features: predominant theta activity located in at least one occipital channel (i.e. by definition spatially restricted and therefore local) lasting from 1 to 15 s. The BERN criteria describe this theta activity as being mostly irregular and polymorphic, 1) with slowing of >1 Hz in case of slow background activity or low voltage EEG, 2) in comparison to wakefulness, poorly delimited or absent alpha activity, with a simultaneous drop-in beta activity, and 3) associated with eye closure of ≥80 % (visually estimated in face videography). These criteria can be supplemented by optional features such as slow eye movements (SEM), decreased muscular tone, and head drops. Although the BERN criteria have built the foundations for standardized assessment of MS episodes, these predominantly EEG-based criteria remain visual, time-consuming, and still lack evidence of scoring reproducibility (in terms of MS episodes duration and whole-brain location) and inter- and intra-scorer variability.

To address this limitation and propose an alternative to visual scoring, several research teams have developed automated real-time detection of MS episodes using EEG signals [42–45], which can even be specific 500 ms before a MS occurs [46]. Such technology is promising but ultimately challenging to implement in clinical practice due the high inter- and intra-individual variability in EEG features, similar to the difficulties faced in the automated scoring of different sleep stages, especially in clinical populations. Other physiologically-based metrics for the easy assessment of MS episodes have been investigated. Of these, pupillary instability [47,48], eyelid closure behavior [49], and the percentage of time when eyes are at least 80 % closed (PERCLOS-80) have shown good results [50–54]. Conversely, metrics based on skin resistance [55] and heart rate variability [56] alone have not yet been shown to be reliable for the detection (or prediction) of MS episodes, but may be useful in combination with other metrics. Future research is needed to achieve a clear consensus and validate newly available metrics against robust outcomes, particularly in the context of clinical EDS assessment.

## 4. Validity of microsleep episodes as markers of sleepiness

The hypothesis that MS episodes serve as markers of sleepiness initially arose from the shared observation of increased low-frequency EEG activity (0.4–12 Hz) in normal sleep onset [57] and MS episodes [58,59]. Both phenomena are part of the neurophysiological continuum of sleepiness, representing discrete moments in which the biological drive to sleep overrides the drive to maintain wakefulness, and may result in a transition from wakefulness to sleep. Supporting this notion, several studies using functional magnetic resonance imaging have indicated that MS episodes occurring during vigilance tasks are associated with mixed patterns of brain activity: decreased neuronal activity in
brain regions related to arousal, such as the thalamus, midbrain and posterior cingulate cortex, contrasting with increased activity in the fronto-parietal, insular, parahippocampal and temporo-occipital cortices that may constitute a mechanism for ending MS episodes and restoring alertness [58–61].

Evidence suggests that analyzing the occurrence and latency of MS episodes during MSLT [62,63] and MWT [64–68] – with most studies applying the 3–15-s duration criteria, (apart from Harrison & Horne 1996 [62] [5–15 s] and Skorucak et al., 2020 [67] [1–15 s]) – may offer a more sensitive and specific assessment of EDS compared with traditional latency measures. One study reported contradictory results, showing that MS latency failed to significantly enhance the correlation between self-reported (ESS score) and objective sleepiness assessment [69]. In that study, and as discussed by the authors, MS latency alone may not be the best variable for sleepiness assessment, and the calculation of a “microsleep index” (which also includes the frequency and duration of MS duration before sleep onset) may represent an interesting alternative.

Stronger data on MS episodes is needed to enhance current recommendations for sleepiness assessment given that there are numerous factors that may influence their occurrence (Fig. 1). Indeed, MS episodes can occur due to circadian rhythm processes, involving factors such as individual chronotype, biological clock, time of the day corresponding to bedtime or naptime, and increased sleep pressure, particularly under conditions of sleep restriction [60,70–76]. Van Egmond et al. [75] indicated that women spent more time in MS episodes than men in sleep restriction conditions. MS episodes can also be influenced by behavioral factors such as the postprandial hours [77], especially after a large meal [78], jet lag [79], shift work [80,81], alcohol consumption [82], and the use of psychoactive agents [83]. It is highly likely that sleep-related medications, such as hypnotics (Z-drugs), are associated with the occurrence of MS episodes, although no clear evidence, apart from attentional lapses [84], has been reported in the literature. Given the

widespread consumption of hypnotics and their recognized impact on daytime vigilance, the link between Z-drugs and MS episodes warrants further investigation.

MS episodes are also common in sleep disorders such as hypersomnia [85], narcolepsy [65,85,86], and sleep apnea syndrome [64,87–89]. Interestingly, the reduction of EDS in patients with narcolepsy treated with modafinil (a dopamine and norepinephrine reuptake inhibitor) [86] or danavorexton/TAK-295 (a selective orexin 2 receptor agonist) [68], and the treatment of severe sleep apnea syndrome with continuous positive airway pressure [88], resulted in substantial subjective and objective improvements in daytime functioning, including the elimination of MS episodes. The intrusion of MS episodes during waking periods may also occur in people with other conditions such as Parkinson’s disease [90] or depression [91]. MS episodes appear to be a reliable but widely neglected marker of sleepiness. Despite their brief and subtle nature, their occurrence can have significant consequences that should not be underestimated.

## 5. Behavioral and cognitive correlates of microsleep episodes

Multiple studies have investigated the behavioral and cognitive impacts of MS episodes using various methodologies. Given the significant public health burden of sleepiness-related road traffic accidents, much of this research has focused on driving performance (see Table 2). Evidence consistently indicates that a higher frequency of MS episodes is associated with impaired driving performance, both in driving simulators [67,81,82,89,92–97] and real road conditions [80,98–102]. Driving performance impairments typically manifest as an increase in the standard deviation of lateral position (indicating loss of vehicle control), greater speed variation, longer reaction times, and a higher incidence of off-road events and crashes (see metrics details in Table 2). However, the validity of driving simulator studies depends on their design and settings, which can substantially influence outcome measures. Moreover,

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Factors influencing microsleep episodes. This figure shows the multiple factors that may contribute to microsleep episodes, categorized into three main domains: circadian and homeostatic (green), behavioral and environmental exposure (gray), and sleep/wake disorders and secondary hypersomnia (red). It should be noted that potential, but not mandatory, interactions may exist between these different layers of influence, and that this list is not exhaustive. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<table><caption>**Table 2**
Summary of articles providing evidence for the effect of electroencephalogram-based or behavioral microsleep on various dimensions of performance.</caption><thead><tr><th>Study</th><th>Microsleep testing</th><th>Performance testing (test duration// performance metrics)</th><th>Main outcomes</th></tr></thead><tbody><tr><td colspan="4"><strong>Driving simulator</strong></td></tr><tr><td>Anderson et al., 2023 [81]</td><td>EEG-based MS (3–15 s)</td><td>120 min (4 wheels)//off-road events</td><td>Increased MS episodes and off-road events in sleep deprivation condition</td></tr><tr><td>Banks et al., 2004 [82]</td><td>EEG-based MS (3–15 s)</td><td>70 min (4 wheels)// off-road events</td><td>Increased MS episodes and off-road events in sleep deprivation + alcohol condition</td></tr><tr><td>Bougard et al., 2020 [97]</td><td>EEG-based MS (3–15 s)</td><td>4 sessions of 50 min every 4 h (2 wheels)//standard deviation of lateral position and falls</td><td>Significant increase in standard deviation of lateral position, falls and MS episodes in sleep deprivation condition</td></tr><tr><td>Boyle et al., 2008 [92]</td><td>EEG-based MS (3–15 s)</td><td>60 min (4 wheels)// standard deviation of lateral position</td><td>Correlation between MS episodes and increased standard deviation of lateral position in sleep apnea syndrome condition</td></tr><tr><td>Chung et al., 2005 [94]</td><td>EEG-based MS (15–30 s) + attention lapses (3–15 s)</td><td>60 min (4 wheels)// off-road events and reaction time</td><td>Strong correlation between MS episodes and off-road events, and between attention lapses and reaction time after surgery condition</td></tr><tr><td>Golz et al., 2013 [95]</td><td>EEG-based MS (1–12 s)</td><td>6 sessions of 40 min every hour (4 wheels)//crashes</td><td>MS episodes were found immediately before 95 % of crashes in sleep deprivation condition</td></tr><tr><td>Moller et al., 2006 [93]</td><td>EEG-based MS (3–15 s)</td><td>4 sessions of 30 min every 2 h (4 wheels)//off-road events</td><td>Strong correlation between MS episodes and off-road events in normal condition</td></tr><tr><td>Risser et al., 2000 [89]</td><td>EEG-based MS (3–15 s)</td><td>60 min (4 wheels)// off-road events</td><td>Strong correlation between MS episodes and off-road events in sleep apnea syndrome condition</td></tr><tr><td>Skorucak et al., 2020 [67]</td><td>EEG-based MS (1–15 s)</td><td>60 min (4 wheels)// off-road events</td><td>Moderate correlation between MS episodes and off-road events in sleep deprivation condition</td></tr><tr><td>Vakulin et al., 2007 [96]</td><td>EEG-based MS (3–15 s)</td><td>70 min (4 wheels)// off-road events</td><td>No significant difference in the number of MS episodes between normal, sleep deprivation and sleep deprivation + alcohol conditions// Significant increase in off-road events in sleep deprivation and sleep deprivation + alcohol conditions</td></tr></tbody></table>

<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Study</th><th>Microsleep testing</th><th>Performance testing (test duration// performance metrics)</th><th>Main outcomes</th></tr></thead><tbody><tr><td colspan="4"><strong>Real-road driving</strong></td></tr><tr><td>Ftouni et al., 2013 [100]</td><td>Behavioral MS: sleep-related events (resting eyes and eyes closures >1 s)</td><td>30 min (4 wheels)// off-road events</td><td>Sleep-related events correlated with a higher incidence of hazardous driving events (increased number of off-road events) in shift work condition</td></tr><tr><td>Kumagai et al., 2023a [98]</td><td>Behavioral MS: eye closures ≥1 s</td><td>Retrospective study with dashcam analysis before crash occurrence</td><td>MS episodes correlated with the occurrence of crashes in normal condition</td></tr><tr><td>Kumagai et al., 2023b [99]</td><td>Behavioral MS: eye closures ≥1 s</td><td>Retrospective study with dashcam analysis before crash occurrence</td><td>MS episodes correlated with the occurrence of crashes in normal condition</td></tr><tr><td>Lee et al., 2016 [80]</td><td>EEG-based MS (3–15 s)</td><td>120 min (4 wheels)//off-road-events</td><td>MS episodes correlated with a higher number of off-road events in night shift work condition</td></tr><tr><td>Mulhall et al., 2020 [101]</td><td>Behavioral MS: eye closure >0.5 s</td><td>40 min (4 wheels)// off-road events</td><td>MS episodes correlated with a higher incidence of hazardous driving events (increased number of off-road events) in night shift condition</td></tr><tr><td colspan="4"><strong>Psychomotor Vigilance Test<sup>a</sup></strong></td></tr><tr><td>Buckley et al., 2016 [103]</td><td>Behavioral MS: flat tracking (0.5–15 s) associated with partial eye closure</td><td>30 min PVT// tracking error</td><td>Increase in MS episodes and errors in normal condition</td></tr><tr><td>Manousakis et al., 2021 [104]</td><td>EEG-based MS (3–15 s)</td><td>10 min PVT// tracking error</td><td>Strong correlation between MS episodes and errors in sleep deprivation condition</td></tr><tr><td colspan="4"><strong>OSLER<sup>a</sup></strong></td></tr><tr><td>Priest et al., 2001 [105]</td><td>EEG-based MS (3–15 s)</td><td>40 min every 2 h// missed responses</td><td>MS episodes were almost always present when ≥4 consecutive stimuli were missed in normal condition</td></tr><tr><td colspan="4"><strong>Cognitive psychomotor performance<sup>b</sup></strong></td></tr><tr><td>Hemmeter et al., 1998 [106]</td><td>EEG-based MS (at least 15 s)</td><td>10-min letter cancellation test// tracking error and speed</td><td>Low cumulative number of MS episodes was associated with significant improvement in performance in depressed and sleep deprived condition</td></tr><tr><td colspan="4"><strong>Continuous tracking task<sup>c</sup></strong></td></tr><tr><td>Peiris et al., 2006 [107]</td><td>EEG-based and behavioral MS (1–30 s)</td><td>1 h CTT//tracking error</td><td>Concomitant increase in MS episodes and tracking errors during the first 30 min but subsequent decreases over the</td></tr></tbody><tfoot><tr><td colspan="4">(continued on next page)</td></tr></tfoot></table>
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Study</th><th>Microsleep testing</th><th>Performance testing (test duration// performance metrics)</th><th>Main outcomes</th></tr></thead><tbody><tr><td>Poudel et al., 2018 [60]</td><td>Behavioral MS: flat tracking (0.5-15 s) associated with partial eye closures</td><td>20-min CTT// tracking error</td><td>second 30 min in normal condition Improvement of tracking error immediately after versus immediately before an MS episode in sleep deprivation condition</td></tr></tbody></table>

Abbreviations: CTT, continuous tracking task; EEG, electroencephalogram; MS, microsleep; OSLER, oxford sleep resistance test; PVT, psychomotor vigilance test.

a Details of the PVT and the OSLER are provided in Table 1.

b A cognitive psychomotor performance test, such as the letter cancellation test, is used to evaluate attention. In the letter cancellation test, participants are given a sheet filled with random letters and instructed to cancel a specific target letter (e.g., ‘E’ or ‘A’) as quickly and accurately as possible within a set time limit. Performance is measured by counting the number of correctly cancelled target letters and noting any errors, including missed targets (omissions) and incorrect cancellations (commissions).

c A continuous tracking task test evaluates sustained attention and hand-eye coordination. Participants use a tracking device, such as a joystick or computer mouse, to follow a moving target on a display screen as closely as possible for a set duration. Performance is measured by the accuracy and consistency of alignment with the target.

the extent to which simulator-based driving performance reflects real-world driving remains uncertain because motivation and risk-taking behavior play critical roles in maintaining wakefulness. Despite these limitations, available data suggest that there is a dose-response relationship between MS episodes and accident risk; while brief and occasional MS episodes may not systematically result in accidents, more prolonged and frequent MS episodes significantly increase the risk of critical driving errors and crash events.

Other studies using various cognitive performance-based tests have reported a positive correlation between MS episodes, missed responses, and longer reaction times in both sleep deprivation and sufficient sleep duration conditions (see Table 2). These findings were substantiated using the PVT [103,104], the OSLER test [105], the letter cancellation test (a cognitive psychomotor performance test) [106], and a continuous tracking task [107]. One study, conducted by Poudel et al., 2018 [60], showed that the number of tracking errors decreased immediately after versus before MS episodes during a continuous tracking task, suggesting that MS episodes may also have potentially restorative beneficial effects. Consistent with this, it has been suggested that MS episodes occurring in a background of wake activity may enable a different cognitive mode that promotes creative thinking [108], boosts problem-solving and performance recovery [109], and more globally influences conscious experience and contributes to mind wandering [110]. All MS episodes are probably not homogenous in terms of neurophysiological and behavioral correlates, particularly regarding their duration (e.g. 1 or 3 s versus longer episodes) and the local or more widespread nature of the episode (with widespread episodes requiring further investigation).

All the evidence reviewed in this paper indicates that MS episodes impact multiple aspects of daily life, which can be summarized in three main dimensions: EEG activity, behavioral manifestations, and overall vigilance performance (Fig. 2). This underscores the unique position of MS episodes at the boundary between wakefulness and sleep, emphasizing the importance of including them in the screening and phenotyping of EDS.

The MSLT and MWT provide valuable opportunities to explore the physiological and clinical correlates of MS episodes by mapping their

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Three-dimensional representation of vigilance states, their EEG characteristics, and cognitive and behavioral correlates, including microsleep episodes. The three axes show: EEG activity (→ from fast to slow), vigilance phenotype and behavioral manifestations (→ from high to low muscle tone), and vigilance performance (including cognitive performance, → from high to low). Axis values are arbitrary and serve to conceptually illustrate how a microstate, such as microsleep, is part of the sleep/wake continuum and has specific behavioral and cognitive correlates. Abbreviations: EEG, electroencephalogram; REM, rapid eye movement.</figcaption></figure>

trajectories throughout different trials or creating a ‘microsleep index’ (e.g. based on the frequency, latency and duration of MS episodes). To achieve this, the automatic identification of MS episodes in the MWT appears feasible based on the BERN criteria and corresponding automated methods, with performance comparable to the inter-rater reliability of trained scorers [44]. Consistent with this, recent research by Tracey et al. introduced 15-s estimates of “hypnodensities” through EEG analysis to establish a ‘sleepiness score’ applicable to the MWT protocol [68]. While MS episodes serve as objective, neurophysiological indicators of sleepiness—particularly relevant for assessing real-time vigilance in operational contexts—they are only one component of a complex construct. Standard tools such as the MSLT and MWT assess sleep propensity and capacity to maintain wakefulness, respectively, in controlled conditions, while subjective scales like the ESS capture perceived sleepiness. In addition, EEG-derived methods such as hypnodensities analysis provide a continuous, probabilistic depiction of transitions between vigilance states, offering richer temporal resolution than traditional staging. Incorporating MS detection alongside these complementary methods may improve the sensitivity and ecological validity of sleepiness assessments, particularly in situations where brief lapses can have significant consequences (Fig. 3).

## 6. Prevention and countermeasures

MS episodes can be hazardous, especially if they occur in high-risk situations such as driving. Therefore, the strategic use of preventive measures and countermeasures to overcome MS episodes should be a public health priority. However, identifying effective approaches for preventing and mitigating MS episodes remains challenging due to the lack of simple and real-time methods for detecting MS episodes. As a result, MS episodes have rarely been investigated as a primary outcome in studies evaluating the impact of specific interventions, and there is therefore a limited body of evidence to support effective and validated
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Proposed integrated model for comprehensive sleepiness assessment including microsleep episodes. This model proposes a rationalized integrated approach to sleepiness evaluation, outlining three distinct care pathways. Pathway 1 applies to cases that do not require extensive evaluation, such as transient sleep deprivation or shift work-related sleepiness. Pathway 2 allows the diagnosis of sleep disorders associated with excessive sleepiness, such as hypersomnia or narcolepsy. Pathway 3 is used to evaluate the treatment response or the fitness to drive in patients undergoing sleep disorder management. At step 2 b, the model incorporates scoring of microsleep episodes within the multiple sleep latency test (MSLT*) and the maintenance of wakefulness test (MWT**), alongside the standard scoring methods. Trajectories and gradient severity thresholds for latency, duration, microsleep index and hypnodensities may help facilitate individualized approaches to prevention and care, including guidance for appropriate sleep hygiene, implementation of countermeasures, and the integration of personalized treatment for "at-risk" patients.</figcaption></figure>

This section aims to provide a comprehensive overview of existing strategies and highlight promising directions for future research.

The most effective approach to preventing MS episodes is ensuring sufficient sleep or, at the very least, incorporating strategic naps (see Table 3). Maintaining an adequate amount of sleep appears to reduce the number of MS episodes and maintain good performance (as evaluated in driving scenarios and PVT) given the strong correlation between sleep deprivation and higher numbers of MS episodes [70,80,81,96,100]. A systematic review published in 2023 reported that driving performance deteriorated significantly in individuals who had slept for <7 h, and those who had slept <5 h in the last 24 h had double the accident risk of those who had slept for ≥8 h [111]. Planned naps have also been shown to reduce the frequency of MS episodes and enhance both pilot performance [112] and performance on the PVT [113,114] (see Table 3). However, defining a sufficient amount of sleep and planned naps may not be the ideal approach for everyone, especially those with underlying pathologies (because studies have included only healthy subjects; see Table 3). For populations with sleep/wake disorders, especially those with hypersomnolence conditions, wake-promoting treatments have been shown to improve subjective and objective sleepiness symptoms [115,116]. Modafinil reduced cognitive performance impairment and suppressed MS episodes in one study [86], and another reported that danavorexton reduced MS episodes in individuals with narcolepsy type 1 and 2, but performance metrics were not reported in that study [68].

A promising approach to preventing MS episodes is to focus on driver education, especially with respect to sleep hygiene and self-assessment of sleepiness before driving. One study reported that drivers who received targeted education on these topics were less likely to

experience MS episodes while driving compared with a control group who had targeted education [117]. A comprehensive sleep hygiene strategy [118], including sufficient sleep, planned naps and education on self-assessment of sleepiness, is a key intervention for preventing MS episodes.

Long-term preventive measures could be complemented by short-term countermeasures as part of a global “wakefulness strategy”. Of the potential options, caffeine consumption is a strong candidate, widely recognized for its ability to enhance alertness, even under conditions of sleep deprivation [119]. Oral intake of a slow-release caffeine tablet effectively reduced MS episodes and improved performance (cognitive functions and motor skills) in a study of 16 healthy male volunteers [120]. Other countermeasures, such as auditory stimulation (e.g. beta frequency tones [121]) and sensory stimulation (e.g. face-washing [122]) have been shown to reduce daytime sleepiness, but there is currently no evidence to directly support their efficacy in mitigating MS episodes.

Although existing and emerging interventions show promise, rigorous clinical trial validation of their ability to prevent or mitigate the impact of MS episodes is essential before these interventions can been recommended as being safe and effective for implementation in the general population.

## 7. Conclusion

Sleepiness is common and is associated with a significant risk of accidents and impaired performance, particularly in a society that increasingly operates 24/7. Despite growing interest in MS episodes in the sleep field, this review reveals critical gaps in current knowledge,
<table><caption>**Table 3**
Summary of articles providing evidence of microsleep episodes countermeasures.</caption><thead><tr><th rowspan="2">Study</th><th>Paradigm</th><th colspan="2">Methodology</th><th colspan="3">Functional outcomes</th></tr><tr><th>Sleep/wake protocol</th><th>Microsleep testing point</th><th>Performance testing point</th><th>Alertness improved?</th><th>Performance improved?</th><th>Global outcome</th></tr></thead><tbody><tr><td colspan="7"><strong>Sufficient sleep</strong></td></tr><tr><td>Anderson et al., 2023 [81]</td><td>Post sleep (≥5 h of sleep between 10pm and 8am) vs. Post night shift</td><td>MS rate: EEG-based (3–15 s)</td><td>Driving simulator (70 min)</td><td>YES; fewer MS episodes</td><td>YES; fewer lane departures</td><td>↗</td></tr><tr><td>Arnal et al., 2015 [70]</td><td>Extended sleep (10 h in bed vs. habitual sleep (8 h in bed)</td><td>MS rate: EEG-based (3–15 s)</td><td>PVT (10 min)</td><td>YES; fewer MS episodes</td><td>YES; fewer lapses and faster speed</td><td>↗</td></tr><tr><td>Ftouni et al., 2013 [100]</td><td>Pre shift (8 h of sleep) vs. post shift (5 h of sleep)</td><td>Sleep-related events (resting eyes and long eye closures)</td><td>Driving simulator (30 min)</td><td>YES; fewer sleep-related events</td><td>YES; fewer lane departures</td><td>↗</td></tr><tr><td>Lee et al., 2016 [80]</td><td>Post sleep (≥5 h of sleep) vs. post night shift</td><td>MS rate: EEG-based (3–15 s)</td><td>Driving simulator (120 min)</td><td>YES; less MS episodes</td><td>YES; less lane departure</td><td>↗</td></tr><tr><td>Poudel et al., 2018 [60]</td><td>Post sleep (8 h of sleep) vs. Post sleep deprivation (4 h of sleep)</td><td>MS rate: flat tracking (0.5–15 s) with partial eye closure</td><td>Continuous tracking task (20 min)</td><td>YES; fewer MS episodes</td><td>YES; fewer tracking errors</td><td>↗</td></tr><tr><td>Vakulin et al., 2007 [96]</td><td>Post sleep (8 h of sleep) vs. post sleep deprivation (4 h of sleep)</td><td>MS rate: EEG-based (3–15 s)</td><td>Driving simulator (120 min)</td><td>NO; fewer MS episodes but not statistically significant</td><td>YES; fewer lane departures</td><td>↗</td></tr><tr><td colspan="7"><strong>Nap</strong></td></tr><tr><td>Hayashi et al., 2005 [113]</td><td>30-min nap with N2 stage sleep</td><td>Occurrence of SEMs</td><td>PVT (10 min)</td><td>YES; decreased occurrence of SEMs</td><td>YES; fewer errors</td><td>↗</td></tr><tr><td>Rosekind et al., 1994 [112]</td><td>40-min nap (in-flight crew members)</td><td>MS rate: EEG-based (5–15 s)</td><td>Airplane piloting performance (all night)</td><td>YES; fewer MS episodes</td><td>YES; faster reaction time</td><td>↗</td></tr><tr><td>Signal et al., 2009 [114]</td><td>40-min nap (air traffic controllers)</td><td>EEG theta and alpha, occurrence of SEMs</td><td>PVT (10 min)</td><td>YES; decreased EEG power and reduced likelihood of SEMs</td><td>YES; faster reaction time</td><td>↗</td></tr><tr><td colspan="7"><strong>Caffeine</strong></td></tr><tr><td>Beaumont et al., 2001 [120]</td><td>Slow-release caffeine vs. placebo</td><td>MS rate: EEG-based (3–15 s)</td><td>Seven tests from the NATO AGARD STRES Battery<sup>a</sup></td><td>YES; fewer MS episodes</td><td>YES; faster reaction time, fewer errors</td><td>↗</td></tr></tbody></table>

Abbreviations: EEG, electroencephalogram; MS, microsleep; PVT, psychomotor vigilance test; SEMs, slow eye movements. Green arrows indicate strong improvement. Orange arrows indicate mild-moderate improvement.

a The NATO AGARD STRES Battery includes seven tests that assess various cognitive and attentional functions, including reaction time, mathematical processing, memory, logical reasoning, working memory, psychomotor coordination, and sustained attention.

particularly in relation to the incorporation of MS episode evaluation in clinical sleep medicine routine, the impact of MS episodes on road accident risk, and the evidence base for measures to prevent or mitigate MS episodes. One important factor that limits research in this area is the lack of a standardized definition for MS episodes, which is needed to ensure reproducibility across studies and sleep laboratories. Addressing this gap requires a two-step approach: 1) incorporating the analysis of MS episodes into routine clinical measures of objective sleepiness such as the MSLT and MWT [123–126]; and 2) improving the methodologies available to detect MS episodes. A promising strategy could be to combine existing MS identification techniques, such as the BERN criteria and hypnodensities measures. Future research should explore the validation and refinement of such approaches, and how these correlate with outcomes measure, to establish a consensus definition and improve clinical and research applications. Furthermore, prioritizing the development of reliable MS episodes detection systems, particularly those

embedded in vehicle environments, is essential from a safety perspective. In addition, identifying the most effective strategies, such as proper sleep hygiene, planned naps, driver education and strategic caffeine consumption, is essential for preventing MS episodes and mitigating the associated driver impairment. These strategies need to be compared with current standardized methods for identifying MS episodes to ensure their effectiveness.

## Author contributions

Conceptualization: AD, SB and JLP. Substantial contribution to interpretation of data: AD, RT, LB, LPD, YD, JLP and SB. Writing – original draft preparation: AD and SB. Writing – review and editing: JLP, RT, LB, LPD and YD. All authors have read and agreed to the published version of the manuscript.

## Practice points

1) Despite significant advancements in characterizing video-polysomnographic markers of microsleep episodes over the past decade, these markers remain absent from AASM recommendations.

2) Microsleep episodes, though short and subtle, serve as biomarkers of sleepiness in neurophysiological tests such as the MSLT and MWT, and are also valuable for assessing altered wakefulness in various tasks and driving performance tests.

3) Microsleep episodes have been linked to an increased risk of accidents, as evidenced by their association with impaired performance in vigilance tasks and different driving scenarios, highlighting the urgent need to evaluate and implement effective countermeasures.
# Research agenda

1) **Update microsleep identification and scoring:** Investigate and compare new methods for identifying sleep stages and microstates, such as microsleep episodes, using the techniques discussed in this manuscript (visual vs. automatic vs. EEG-based vs. surrogate). The reproducibility of these methods need to be assessed to determine to most accurate approach for the assessment of sleep dynamics and the phenotyping of sleepiness.

2) **Improve safety in the transport industry:** Develop real-time microsleep-warning devices to reduce sleepiness-related adverse events. Such devices could issue alerts to awaken users from impending microsleep episodes, potentially preventing accidents in high-risk occupations (e. g. commercial truck drivers, pilots, air-traffic controllers, and medical staff).

3) **Evaluate countermeasures:** Conduct research to assess the effectiveness of countermeasures against microsleep episodes. Such studies could significantly contribute to enhance global public health and road safety for all users.

## Declaration of competing interest

SB has received financial support for seminars, and congress travel from Bioprojet and Jazz Pharmaceutical. JLP reports income related to medical education from Resmed, Sefam, Zoll-Respicardia, Eli Lilly, Idorsia, Pharmanovia and Bioprojet. YD has received financial support for seminars, board engagements and travel to conferences from Jazz Pharmaceutical, Idorsia, Takeda, Centessa, Avadel and Bioprojet. LPD has received financial support for seminars and congress travel from Bioprojet, Jazz Pharmaceutical and Zogenix, and a research grant from Asten Santé. LB has received financial support for traveling to conferences from Idorsia and Bioprojet, and board engagements from Jazz Pharmaceutical, Takeda, Idorsia and Bioprojet. RT has received research grant from Resmed, Inspire and Bioprojet, and financial support for board engagement and congress travel from Perimetres, Resmed, Inspire, Bioprojet and Idorsia. AD has no competing interests to declare.

## Acknowledgements

The authors would like to address their sincere and heartfelt thanks to Marion Fages for the scientific discussions and unconditional support regarding the microsleep project. English language editing assistance was provided by Nicola Ryan, independent medical writer, funded by “Agir pour les Maladies Chroniques”. We acknowledge financial support from Core for Tech. JLP, SB and RT are supported by the French National Research Agency (ANR-15-IDEX-0002) and the “e-health and integrated care and trajectories medicine and MIAI artificial intelligence (ANR-19-P3IA-0003)” Chairs of excellence from the Grenoble Alpes University Foundation.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.smrv.2025.102113.

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