{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T16:56:34Z","timestamp":1790614594643,"version":"4.1.0"},"reference-count":63,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002343","name":"Institut de recherche Robert-Sauv\u00e9 en sant\u00e9 et en s\u00e9curit\u00e9 du travail","doi-asserted-by":"publisher","award":["2020-0006"],"award-info":[{"award-number":["2020-0006"]}],"id":[{"id":"10.13039\/501100002343","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2020-0006"],"award-info":[{"award-number":["2020-0006"]}],"id":[{"id":"https:\/\/ror.org\/04e4jbv10","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Decreased vigilance associated with drowsiness is a major cause of motor vehicle crashes, but its physiological characterization and detection remain difficult due to behavioral and environmental confounding factors. This study presents an original experimental framework combining a highly controlled sleep-deprivation protocol, repeated driving simulations, and a machine-learning-based heart rate variability (HRV) analysis to study the physiological and behavioral changes in vigilance during a prolonged period of wakefulness while driving. Twelve healthy young adults participated in a 30-h sleep-deprivation constant routine protocol that included driving simulations every two hours, with data collection: electrocardiography (ECG), subjective ratings of drowsiness, psychomotor vigilance test, and driving performance. The collected data was separated into two clearly identifiable vigilance states, a \u201cRested\u201d and a \u201cTired\u201d state, respectively, for the first and last driving sessions, of the protocol. Heart rate variability (HRV) features were extracted from ECG recordings, statistically analyzed, and submitted into supervised machine learning classifiers using participant-independent validation to assess their ability to distinguish between two experimentally defined states of vigilance. The accuracy of state separation was verified based on significant alterations observed in drowsiness ratings and psychomotor performance in the Tired compared to the Rested state. The variability in driving behavior, an indicator of impaired driving ability, increased significantly from the Rested compared to the Tired state. Several HRV features revealed significant differences between Rested and Tired states, with effect sizes ranging from moderate to large, observed for several metrics. Participant-independent machine learning analysis, namely Leave-One-Subject-Out Cross-Validation (LOSO-CV), showed that HRV metrics enable reliable discrimination between vigilance states, with all classifiers evaluated achieving average accuracy greater than 85%. This pilot study demonstrated that the proposed experimental framework, by reducing the major behavioral and environmental confounding factors, can generate two distinct states of vigilance at the physiological and behavioral levels, enabling reliable separation of experimental data, independent of any additional ground truths. The states separated by the framework enable the detection of decreased driving performance and efficiently label HRV data for vigilance classification. Nevertheless, the findings should be interpreted within the context of a pilot study conducted with a small cohort of healthy young adults.<\/jats:p>","DOI":"10.3390\/s26196104","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:57:57Z","timestamp":1790611077000},"page":"6104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tracking Vigilance While Driving: Pilot Study of Heart Rate Variability Classification Under a Controlled Sleep-Deprivation Protocol"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4636-5076","authenticated-orcid":false,"given":"James Elber","family":"Duverger","sequence":"first","affiliation":[{"name":"Institut de Recherche Robert-Sauv\u00e9 en Sant\u00e9 et en S\u00e9curit\u00e9 du Travail, Montr\u00e9al, QC H3A 3C2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Eli","family":"Moser","sequence":"additional","affiliation":[{"name":"Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montr\u00e9al, QC H4H 1R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9367-2919","authenticated-orcid":false,"given":"Philippe","family":"Boudreau","sequence":"additional","affiliation":[{"name":"Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montr\u00e9al, QC H4H 1R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Marie Claude","family":"Ouimet","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Sciences, University of Sherbrooke, Longueuil, QC J4K 0A8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3896-2710","authenticated-orcid":false,"given":"Diane B.","family":"Boivin","sequence":"additional","affiliation":[{"name":"Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montr\u00e9al, QC H4H 1R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4816-6539","authenticated-orcid":false,"given":"Alireza","family":"Saidi","sequence":"additional","affiliation":[{"name":"Institut de Recherche Robert-Sauv\u00e9 en Sant\u00e9 et en S\u00e9curit\u00e9 du Travail, Montr\u00e9al, QC H3A 3C2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"key":"ref_1","unstructured":"Bayne, A., Trivedi, N., Liotta, M., Siegfried, A., Gaspar, J., and Carney, C. (2022). Countermeasures to Reduce Drowsy Driving: Results of a Literature Review and Discussions with Experts, AAA Foundation for Traffic Safety."},{"key":"ref_2","unstructured":"Transport Canada (2023). Road Safety in Canada\u2014Improve Your Safety."},{"key":"ref_3","first-page":"502","article-title":"Driver Fatigue and Road Safety-Implication in an Indian Context","volume":"9","author":"Dagli","year":"2016","journal-title":"Int. J. Adv. Eng. Technol."},{"key":"ref_4","unstructured":"Tefft, B. (2024). Drowsy Driving in Fatal Crashes, United States, 2017\u20132021, AAA Foundation for Traffic Safety."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lo, J.C., Groeger, J.A., Santhi, N., Arbon, E.L., Lazar, A.S., Hasan, S., Von Schantz, M., Archer, S.N., and Dijk, D.-J. (2012). Effects of partial and acute total sleep deprivation on performance across cognitive domains, individuals and circadian phase. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0045987"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1177\/0748730409332068","article-title":"Practical interventions to promote circadian adaptation to permanent night shift work: Study 4","volume":"24","author":"Smith","year":"2009","journal-title":"J. Biol. Rhythm."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1111\/j.1365-2869.2011.00924.x","article-title":"Mismatch between subjective alertness and objective performance under sleep restriction is greatest during the biological night","volume":"21","author":"Zhou","year":"2012","journal-title":"J. Sleep Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/B978-0-444-53817-8.00010-4","article-title":"Predicting cognitive impairment and accident risk","volume":"Volume 190","author":"Raslear","year":"2011","journal-title":"Progress in Brain Research"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1046\/j.1365-2869.2003.00346.x","article-title":"Sleep\u2013wake rhythm in an irregular shift system","volume":"12","author":"Sallinen","year":"2003","journal-title":"J. Sleep Res."},{"key":"ref_10","first-page":"155","article-title":"Self-ReportSleepLength: A Meta-AnalyticReview","volume":"23","author":"Pilcher","year":"2000","journal-title":"Sleep"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"590","DOI":"10.5271\/sjweh.3313","article-title":"Characteristics of the Million Women Study participants who have and have not worked at night","volume":"38","author":"Wang","year":"2012","journal-title":"Scand. J. Work. Environ. Health"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1108\/IJWHM-04-2025-0078","article-title":"\u201cToo tired to care\u201d\u2014The experiences of doctors involved in car accidents after working extended shifts","volume":"18","author":"Borg","year":"2025","journal-title":"Int. J. Workplace Health Manag."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Di Milia, L., Rogers, N.L., and \u00c5kerstedt, T. (2012). Sleepiness, long distance commuting and night work as predictors of driving performance. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0045856"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1007\/s00420-022-01855-7","article-title":"The relationship between burnout, commuting crashes and drowsy driving among hospital health care workers","volume":"95","author":"HaGani","year":"2022","journal-title":"Int. Arch. Occup. Environ. Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e13730","DOI":"10.1111\/jsr.13730","article-title":"The impact of break duration, time of break onset, and prior shift duration on the amount of sleep between shifts in heavy vehicle drivers","volume":"32","author":"Harris","year":"2023","journal-title":"J. Sleep Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1108\/PIJPSM-03-2015-0033","article-title":"Police drowsy driving: Predicting fatigue-related performance decay","volume":"38","author":"James","year":"2015","journal-title":"Polic. Int. J. Police Strateg. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.5665\/sleep.2214","article-title":"A combined field and laboratory design for assessing the impact of night shift work on police officer operational performance","volume":"35","author":"Waggoner","year":"2012","journal-title":"Sleep"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Albadawi, Y., Takruri, M., and Awad, M. (2022). A review of recent developments in driver drowsiness detection systems. Sensors, 22.","DOI":"10.3390\/s22052069"},{"key":"ref_19","unstructured":"Nasri, I., Karrouchi, M., Kassmi, K., and Messaoudi, A. (2022). A review of driver drowsiness detection systems: Techniques, advantages and limitations. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1007\/s11571-022-09898-9","article-title":"A systematic review of physiological signals based driver drowsiness detection systems","volume":"17","author":"Saleem","year":"2023","journal-title":"Cogn. Neurodyn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"24898","DOI":"10.1038\/s41598-025-08582-2","article-title":"Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning","volume":"15","author":"AlArnaout","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shaffer, F., and Ginsberg, J.P. (2017). An overview of heart rate variability metrics and norms. Front. Public Health, 5.","DOI":"10.3389\/fpubh.2017.00258"},{"key":"ref_23","unstructured":"Waxenbaum, J.A., Reddy, V., and Das, J.M. (2025). Anatomy, autonomic nervous system. StatPearls [Internet], StatPearls Publishing."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.cophys.2020.01.002","article-title":"Sleep and autonomic nervous system","volume":"15","author":"Zoccoli","year":"2020","journal-title":"Curr. Opin. Physiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s11517-015-1448-7","article-title":"Drowsiness detection using heart rate variability","volume":"54","author":"Vicente","year":"2016","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ebrahimian, S., Nahvi, A., Tashakori, M., Salmanzadeh, H., Mohseni, O., and Lepp\u00e4nen, T. (2022). Multi-level classification of driver drowsiness by simultaneous analysis of ECG and respiration signals using deep neural networks. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph191710736"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"81826","DOI":"10.1109\/ACCESS.2019.2924481","article-title":"Driver drowsiness detection based on respiratory signal analysis","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17793","DOI":"10.1007\/s11042-020-08696-x","article-title":"Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator","volume":"79","author":"Kiashari","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tateno, S., Guan, X., Cao, R., and Qu, Z. (2018, January 14). Development of drowsiness detection system based on respiration changes using heart rate monitoring. Proceedings of the 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Nara, Japan.","DOI":"10.23919\/SICE.2018.8492599"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1080\/15389588.2018.1548766","article-title":"Deriving heart rate variability indices from cardiac monitoring\u2014An indicator of driver sleepiness","volume":"20","author":"Buendia","year":"2019","journal-title":"Traffic Inj. Prev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1109\/TBME.2018.2879346","article-title":"Heart rate variability-based driver drowsiness detection and its validation with EEG","volume":"66","author":"Fujiwara","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Salvati, L., d\u2019Amore, M., Fiorentino, A., Pellegrino, A., Sena, P., and Villecco, F. (2021). On-road detection of driver fatigue and drowsiness during medium-distance journeys. Entropy, 23.","DOI":"10.3390\/e23020135"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zeng, C., Wang, W., Chen, C., Zhang, C., and Cheng, B. (2020). Sex differences in time-domain and frequency-domain heart rate variability measures of fatigued drivers. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17228499"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Arefnezhad, S., Eichberger, A., Fr\u00fchwirth, M., Kaufmann, C., and Moser, M. (2020, January 11\u201314). Driver drowsiness classification using data fusion of vehicle-based measures and ECG signals. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9282867"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Awais, M., Badruddin, N., and Drieberg, M. (2017). A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability. Sensors, 17.","DOI":"10.3390\/s17091991"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"El Abbaoui, A., Sodoyer, D., and Elbahhar, F. (2023). Contactless heart and respiration rates estimation and classification of driver physiological states using CW radar and temporal neural networks. Sensors, 23.","DOI":"10.3390\/s23239457"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.jsr.2021.12.001","article-title":"Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches","volume":"80","author":"Hasan","year":"2022","journal-title":"J. Saf. Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kundinger, T., Sofra, N., and Riener, A. (2020). Assessment of the potential of wrist-worn wearable sensors for driver drowsiness detection. Sensors, 20.","DOI":"10.3390\/s20041029"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lee, H., Lee, J., and Shin, M. (2019). Using wearable ECG\/PPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots. Electronics, 8.","DOI":"10.3390\/electronics8020192"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16494","DOI":"10.3390\/s131216494","article-title":"Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier","volume":"13","author":"Li","year":"2013","journal-title":"Sensors"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, C., Zhu, X., Wang, R., Zhou, W., Li, N., and Xie, Y. (2025). Early driver fatigue detection system: A cost-effective and wearable approach utilizing embedded machine learning. Vehicles, 7.","DOI":"10.3390\/vehicles7010003"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1109\/TITS.2020.2981941","article-title":"Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions","volume":"22","author":"Persson","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"zsae199","DOI":"10.1093\/sleep\/zsae199","article-title":"Tracking vigilance fluctuations in real-time: A sliding-window heart rate variability-based machine-learning approach","volume":"48","author":"Xie","year":"2025","journal-title":"Sleep"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"113204","DOI":"10.1016\/j.eswa.2020.113204","article-title":"Driver fatigue transition prediction in highly automated driving using physiological features","volume":"147","author":"Zhou","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Halomoan, J., Ramli, K., Sudiana, D., Gunawan, T.S., and Salman, M. (2023). ECG-based driving fatigue detection using heart rate variability analysis with mutual information. Information, 14.","DOI":"10.3390\/info14100539"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chang, R.C.-H., Wang, C.-Y., Chen, W.-T., and Chiu, C.-D. (2022). Drowsiness detection system based on PERCLOS and facial physiological signal. Sensors, 22.","DOI":"10.3390\/s22145380"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/B978-0-12-396971-2.00007-5","article-title":"Circadian rhythms, sleep deprivation, and human performance","volume":"119","author":"Goel","year":"2013","journal-title":"Prog. Mol. Biol. Transl. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"McMahon, W.R., Ftouni, S., Phillips, A.J., Beatty, C., Lockley, S.W., Rajaratnam, S.M., Maruff, P., Drummond, S.P., and Anderson, C. (2020). The impact of structured sleep schedules prior to an in-laboratory study: Individual differences in sleep and circadian timing. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0236566"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Dickinson, D.L., Drummond, S.P., and McElroy, T. (2017). The viability of an ecologically valid chronic sleep restriction and circadian timing protocol: An examination of sample attrition, compliance, and effectiveness at impacting sleepiness and mood. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174367"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1177\/074873002129002294","article-title":"Getting through to circadian oscillators: Why use constant routines?","volume":"17","author":"Duffy","year":"2002","journal-title":"J. Biol. Rhythm."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e12745","DOI":"10.1111\/jpi.12745","article-title":"A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data","volume":"71","author":"Brown","year":"2021","journal-title":"J. Pineal Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"eadj6834","DOI":"10.1126\/sciadv.adj6834","article-title":"Accurate detection of acute sleep deprivation using a metabolomic biomarker\u2014A machine learning approach","volume":"10","author":"Jeppe","year":"2024","journal-title":"Sci. Adv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e2212685120","DOI":"10.1073\/pnas.2212685120","article-title":"Machine learning estimation of human body time using metabolomic profiling","volume":"120","author":"Woelders","year":"2023","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11906-025-01345-4","article-title":"Methods for Assessment of Sleep and Circadian Rhythms in Cardiovascular Research","volume":"27","author":"Williams","year":"2025","journal-title":"Curr. Hypertens. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Daley, M.S., Gever, D., Posada-Quintero, H.F., Kong, Y., Chon, K., and Bolkhovsky, J.B. (2020). Machine learning models for the classification of sleep deprivation induced performance impairment during a psychomotor vigilance task using indices of eye and face tracking. Front. Artif. Intell., 3.","DOI":"10.3389\/frai.2020.00017"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e2103070118","DOI":"10.1073\/pnas.2103070118","article-title":"Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function","volume":"118","author":"Gardiner","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_57","unstructured":"First, M., Williams, J., Karg, R., and Spitzer, R. (2015). Structured Clinical Interview for DSM-5\u2014Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV), American Psychiatric Association."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"107322","DOI":"10.1016\/j.aap.2023.107322","article-title":"A randomized controlled pilot trial of brief online mindfulness training in young drivers","volume":"193","author":"Albert","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1080\/00140139.2023.2205620","article-title":"Visual and steering behaviours during lane departures: A longitudinal study of interactions between lane departure warning system, driving task and driving experience","volume":"67","author":"Navarro","year":"2024","journal-title":"Ergonomics"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.sleep.2022.01.019","article-title":"REM sleep latency changes after version 2.1 of the AASM manual for scoring sleep","volume":"90","author":"Maya","year":"2022","journal-title":"Sleep Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1111\/j.1365-2869.1992.tb00021.x","article-title":"Circadian and sleep\/wake dependent aspects of subjective alertness and cognitive performance","volume":"1","author":"DIJK","year":"1992","journal-title":"J. Sleep Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1093\/sleep\/34.1.57","article-title":"Dynamics of neurobehavioral performance variability under forced desynchrony: Evidence of state instability","volume":"34","author":"Zhou","year":"2011","journal-title":"Sleep"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real time QRS detection algorithm","volume":"3","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:59:53Z","timestamp":1790611193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,26]]},"references-count":63,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196104"],"URL":"https:\/\/doi.org\/10.3390\/s26196104","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,26]]}}}