Objective: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. Methods: Changes in sleep condition affect the autonomic nervous system (ANS) and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram (ECG) trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control (MSPC) which is a well-known anomaly detection method. Result: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 drowsy episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. Conclusion: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. Significance: The proposed method can contribute to prevent accidents caused by drowsy driving.

Synchronous oscillations of thousands of cellular clocks in the suprachiasmatic nucleus (SCN), the circadian centre, are coordinated by precisely timed cell-cell communication, the principle of which is largely unknown. Here we show that the amount of RGS16 (regulator of G protein signalling 16), a protein known to inactivate Gαi, increases at a selective circadian time to allow time-dependent activation of intracellular cyclic AMP signalling in the SCN. Gene ablation of Rgs16 leads to the loss of circadian production of cAMP and as a result lengthens circadian period of behavioural rhythm. The temporally precise regulation of the cAMP signal by clock-controlled RGS16 is needed for the dorsomedial SCN to maintain a normal phase-relationship to the ventrolateral SCN. Thus, RGS16-dependent temporal regulation of intracellular G protein signalling coordinates the intercellular synchrony of SCN pacemaker neurons and thereby defines the 24 h rhythm in behaviour.

Nocturnal enuresis in children and nocturia in the elderly are two highly prevalent clinical conditions characterized by a mismatch between urine production rate in the kidneys and storage in the urinary bladder during the sleep phase. Here we demonstrate, using a novel method for automated recording of mouse micturition, that connexin43, a bladder gap junction protein, is a negative regulator of functional bladder capacity. Bladder connexin43 levels and functional capacity show circadian oscillations in wild-type mice, but such rhythms are completely lost in Cry-null mice having a dysfunctional biological clock. Bladder muscle cells have an internal clock, and show oscillations of connexin43 and gap junction function. A clock regulator, Rev-erbα, upregulates connexin43 transcription as a cofactor of Sp1, using Sp1 cis-elements of the promoter. Therefore, circadian oscillation of connexin43 is associated with the biological clock and contributes to diurnal changes in bladder capacity, which avoids disturbance of sleep by micturition.

The Athens Insomnia Scale (AIS) can be regarded as a highly useful instrument in both clinical and research settings, except for when assessing the severity level. This study aims to determine the severity criteria for AIS by using the Insomnia Severity Index (ISI). A total of 1666 government employees aged 20 years or older were evaluated using the AIS and ISI, the Patient Health Questionnaire for depressive symptoms, the Epworth Sleepiness Scale for daytime sleepiness, and the Short Form Health Survey of the Medical Outcomes Study for health-related quality of life (QoL). A significant positive correlation (r) was found between the AIS and the ISI (r = 0.80, p < 0.001). As a result of describing receiver–operator curves, the severity criteria of the AIS are capable of categorizing insomnia severity as follows: absence of insomnia (0–5), mild insomnia (6–9), moderate insomnia (10–15), and severe insomnia (16–24). In addition, compared to all scales across groups categorized by AIS or ISI, it was revealed that similar results could be obtained (all p < 0.05). Therefore, the identification of the severity of AIS in this study is important in linking the findings of epidemiological studies with those of clinical studies.

Background: Humans spend more than a fourth of their life sleeping, and sleep quality has been significantly linked to health. However, the objective examination of ambulatory sleep quality remains a challenge, since sleep is a state of unconsciousness, which limits the reliability of self-reports. Therefore, a non-invasive, continuous, and objective method for the recording and analysis of naturalistic sleep is required. Objective: Portable sleep recording devices provide a suitable solution for the ambulatory analysis of sleep quality. In this study, the performance of two activity-based sleep monitors (Actiwatch and MTN-210) and a single-channel EEG-based sleep monitor (SleepScope) were compared in order to examine their reliability for the assessment of sleep quality. Methods: Twenty healthy adults were recruited for this study. First, data from daily activity recorded by Actiwatch and MTN-210 were compared to determine whether MTN-210, a more affordable device, could yield data similar to Actiwatch, the de-facto standard. In addition, sleep detection ability was examined using data obtained by polysomnography as reference. One simple analysis included comparing the sleep/wake detection ability of Actiwatch, MTN-210, and SleepScope. Furthermore, the fidelity of sleep stage determination was examined using SleepScope in finer time resolution. Results: The results indicate that MTN-210 demonstrates an activity pattern comparable to that of Actiwatch, although their sensitivity preferences were not identical. Moreover, MTN-210 provides assessment of sleep duration comparable to that of the wrist-worn Actiwatch when MTN-210 was attached to the body. SleepScope featured superior overall sleep detection performance among the three methods tested. Furthermore, SleepScope was able to provide information regarding sleep architecture, although systemic bias was found. Conclusion: The present results suggest that single-channel EEG-based sleep monitors are the superior option for the examination of naturalistic sleep. The current results pave a possible future use for reliable portable sleep assessment methods in an ambulatory rather than a laboratory setting.