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 and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, 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 pre-N1 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 preventing accidents caused by drowsy driving.

BackgroundHumans spend more than one-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.ObjectivePortable 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 electroencephalography (EEG)-based sleep monitor (SleepScope) were compared in order to examine their reliability for the assessment of sleep quality.MethodsTwenty 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.ResultsThe 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.ConclusionThe 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.

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.

Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.

Background: Insomnia has a high prevalence in modern society. Various tools have been developed to assess insomnia. We performed a direct comparison between the Insomnia Severity Index (ISI) and the Athens Insomnia Scale (AIS) in a Japanese population. Methods: A cross-sectional questionnaire-based study was conducted in September 2017 as part of the Night in Japan Home Sleep Monitoring Study. In addition to insomnia, assessed using the AIS and ISI, depression, sleepiness, quality of life, and work performance were assessed using the Patient Health Questionnaire (PHQ)-9, a Japanese version of the Epworth Sleepiness Scale, the Short Form-8 Health Survey Questionnaire (SF-8), and the World Health Organization Health and Work Performance Questionnaire, respectively. Receiver operating characteristic (ROC) curves were constructed to compare the outcomes of the AIS and the ISI. Results: A total of 1685 (81.9%) of all eligible employees were enrolled. The total scores of the AIS and ISI had a Pearson correlation coefficient (r) of 0.80 (p < 0.01). The area under the ROC curve for the AIS and ISI for the detection of depression (PHQ-9 ≥ 10) was 0.89 and 0.86, respectively. The prevalence of clinical insomnia (ISI ≥ 15) and definite insomnia (AIS ≥ 10) were 6.5 and 10.8%, respectively. Both the AIS and ISI showed a weak negative correlation with the physical component summary score of the SF-8 (r = − 0.37, p < 0.01 and r = − 0.32, p < 0.01, respectively) and absolute presenteeism (r = − 0.32, p < 0.01 and r = − 0.28, p < 0.01, respectively) and a moderate negative correlation with the mental component summary score of the SF-8 (r = − 0.53, p < 0.01 and r = − 0.43, p < 0.01, respectively). Conclusions: A strong positive correlation was found between the total scores of the AIS and ISI. Both the AIS and ISI were found to be associated with low physical and mental quality of life, depression, and productivity loss at work. Moreover, they had a moderate accuracy for detecting depression. Both the AIS and ISI may serve as useful screening tools for both insomnia and depression in the Japanese working population. Trial registration: UMIN-CTR (UMIN000028675, registered on 2017/8/15) and ClinicalTrials.gov (NCT03276585, registered on 2017/9/3).