{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:47:12Z","timestamp":1787068032382,"version":"3.56.0"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T00:00:00Z","timestamp":1504137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["HICOE"],"award-info":[{"award-number":["HICOE"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF\/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p &lt; 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system\u2019s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.<\/jats:p>","DOI":"10.3390\/s17091991","type":"journal-article","created":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T10:54:44Z","timestamp":1504176884000},"page":"1991","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":253,"title":["A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6421-9245","authenticated-orcid":false,"given":"Muhammad","family":"Awais","sequence":"first","affiliation":[{"name":"Centre for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Malaysia"},{"name":"Department of Electrical, Electronic, and Information Engineering Guglielmo Marconi, University of Bologna, Bologna 40126, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasreen","family":"Badruddin","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Malaysia"},{"name":"Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8417-6780","authenticated-orcid":false,"given":"Micheal","family":"Drieberg","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,31]]},"reference":[{"key":"ref_1","unstructured":"(2017, April 15). Traffic Safety Facts, Available online: https:\/\/crashstats.nhtsa.dot.gov\/Api\/Public\/Publication\/811449."},{"key":"ref_2","unstructured":"(2017, March 30). State of the Road Fatigue Fact Sheet. Available online: https:\/\/www.fatiguemanagementtrainingonline.com.au\/blog\/state-of-the-road-fatigue-fact-sheet\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16937","DOI":"10.3390\/s121216937","article-title":"Detecting driver drowsiness based on sensors: A review","volume":"12","author":"Sahayadhas","year":"2012","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.mehy.2008.05.035","article-title":"A definition of drowsiness: One purpose for sleep?","volume":"71","author":"Slater","year":"2008","journal-title":"Med. Hypotheses"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"130","DOI":"10.4103\/2228-7477.95297","article-title":"EEG-based drowsiness detection for safe driving using chaotic features and statistical tests","volume":"1","author":"Mardi","year":"2011","journal-title":"J. Med. Signals Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tun\u00e7er, O., G\u00fcven\u00e7, L., and Co\u015fkun, F. (2010, January 10\u201313). Vision based lane keeping assistance control triggered by a driver inattention monitor. Proceedings of the International Conference on Systems Man and Cybernetics (SMC), Istanbul, Turkey.","DOI":"10.1109\/ICSMC.2010.5642254"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1243\/0954407011528536","article-title":"Unobtrusive drowsiness detection by neural network learning of driver steering","volume":"215","author":"Sayed","year":"2001","journal-title":"J. Automob. Eng."},{"key":"ref_9","unstructured":"Pomerleau, D. (1995, January 25\u201326). RALPH: Rapidly adapting lateral position handler. Proceedings of the Intelligent Vehicles\u2019 Symposium, Detroit, MI, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/S0001-4575(02)00014-3","article-title":"Monotony of road environment and driver fatigue: A simulator study","volume":"35","author":"Thiffault","year":"2003","journal-title":"Accid. Anal. Prev."},{"key":"ref_11","unstructured":"Vural, E. (2009). Video-based detection of driver fatigue. [Ph.D. Thesis, Sabanci University]."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khushaba, R.N., Kodagoda, S., Lal, S., and Dissanayake, G. (2011, January 25\u201330). Intelligent driver drowsiness detection system using Uncorrelated Fuzzy Locality Preserving Analysis. Proceedings of the Intelligent Robots and Systems (IROS) Conference, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048051"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shahid, A., Wilkinson, K., and Marcu, S. (2012). Stanford Sleepiness Scale (SSS), STOP, THAT and One Hundred Other Sleep Scales, Springer. [1st ed.].","DOI":"10.1007\/978-1-4419-9893-4"},{"key":"ref_14","unstructured":"(2017, February 02). Stanford Sleepiness Scale. Available online: http:\/\/www.stanford.edu\/~dement\/sss.html."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shahid, A., Wilkinson, K., Marcu, S., Colin, M., and Shapiro, M. (2012). Karolinska Sleepiness Scale (KSS) in STOP, THAT and One Hundred Other Sleep Scales, Springer. [1st ed.].","DOI":"10.1007\/978-1-4419-9893-4"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s00213-011-2549-0","article-title":"Effects of dexamphetamine with and without alcohol on simulated driving","volume":"222","author":"Simons","year":"2012","journal-title":"Psychopharmacology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.apergo.2010.04.005","article-title":"The relationship between subjective and objective sleepiness and performance during a simulated night-shift with a nap countermeasure","volume":"42","author":"Tremaine","year":"2010","journal-title":"Appl. Ergon."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.sleep.2007.02.001","article-title":"Awareness of sleepiness and ability to predict sleep onset: Can drivers avoid falling asleep at the wheel?","volume":"9","author":"Kaplan","year":"2007","journal-title":"Sleep Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Azim, T., Jaffar, M.A., and Mirza, A.M. (2009, January 7\u20139). Automatic Fatigue Detection of Drivers through Pupil Detection and Yawning Analysis. Proceedings of the 4th Innovative Computing, Information and Control (ICICIC), Kaohsiung, Taiwan.","DOI":"10.1109\/ICICIC.2009.119"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Danisman, T., Danisman, T., Bilasco, I.M., Djeraba, C., and Ihaddadene, N. (2010, January 3\u20135). Drowsy driver detection system using eye blink patterns. Proceedings of the International Conference on Machine and Web Intelligence (ICMWI), Algiers, Algeria.","DOI":"10.1109\/ICMWI.2010.5648121"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fan, X., Yin, B.C., and Sun, Y.F. (2007, January 19\u201322). Yawning detection for monitoring driver fatigue. Proceedings of the International Conference on Machine Learning Cybernetics (ICMLC), Hong Kong, China.","DOI":"10.1109\/ICMLC.2007.4370228"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lee, D., Oh, S., Heo, S., and Hahn, M. (2008, January 15\u201316). Drowsy driving detection based on the driver\u2019s head movement using infrared sensors. Proceedings of the 2nd International Symposium on Universal Communication, Osaka, Japan.","DOI":"10.1109\/ISUC.2008.76"},{"key":"ref_23","unstructured":"Popieul, J.C., Simon, P., and Loslever, P. (2003, January 9\u201311). Using driver\u2019s head movements evolution as a drowsiness indicator. Proceedings of the IEEE Intelligent Vehicles Symposium, Columbus, OH, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ying, Y., Jing, S., and Wei, Z. (2007, January 5\u20138). The monitoring method of driver\u2019s fatigue based on neural network. Proceedings of the International Conference on Mechatronics and Automation, Harbin, China.","DOI":"10.1109\/ICMA.2007.4304136"},{"key":"ref_25","unstructured":"Yu, X. (2009). Real-Time Nonintrusive Detection of Driver Drowsiness, University of Minnesota. CTS 09-15 Technical Report."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1109\/TBCAS.2010.2046415","article-title":"A real-time wireless brain\u2013computer interface system for drowsiness detection","volume":"4","author":"Lin","year":"2010","journal-title":"IEEE Trans. Biomed. Circuit Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2726","DOI":"10.1109\/TCSI.2005.857555","article-title":"EEG-based drowsiness estimation for safety driving using independent component analysis","volume":"52","author":"Lin","year":"2005","journal-title":"IEEE Trans. Biomed. Circuit Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1016\/j.clinph.2007.04.031","article-title":"Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents","volume":"118","author":"Papadelis","year":"2007","journal-title":"Clinic. Neurophysiol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Michail, E., Kokonozi, A., Chouvarda, I., and Maglaveras, N. (2008, January 25\u201328). EEG and HRV markers of sleepiness and loss of control during car driving. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4649724"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1016\/j.ins.2010.01.011","article-title":"A driver fatigue recognition model based on information fusion and dynamic Bayesian network","volume":"180","author":"Yang","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_31","first-page":"764","article-title":"On-line detection of drowsiness using brain and visual information, Systems, Man and Cybernetics, Part A: Systems and Humans","volume":"42","author":"Picot","year":"2012","journal-title":"IEEE Trans. Syst. Hum."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1093\/sleep\/22.8.1067","article-title":"Heart rate variability during waking and sleep in healthy males and females","volume":"22","author":"Elsenbruch","year":"1999","journal-title":"Sleep"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1046\/j.1440-1819.2001.00862.x","article-title":"Effects of light and sleep stages on heart rate variability in humans","volume":"55","author":"Tsunoda","year":"2001","journal-title":"Psychiatry Clin. Neurosci."},{"key":"ref_34","unstructured":"Hu, S., Bowlds, R.L., Ye, G., and Yu, X. (2009, January 3\u20136). Pulse wave sensor for non-intrusive driver\u2019s drowsiness detection. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, MN, USA."},{"key":"ref_35","unstructured":"Furman, G.D., and Baharav, A. (2010, January 26\u221229). Investigation of drowsiness while driving utilizing analysis of heart rate fluctuations. Proceedings of the IEEE Computing in Cardiology, Belfast, UK."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s13246-013-0200-6","article-title":"Drowsiness detection during different times of day using multiple features","volume":"36","author":"Sahayadhas","year":"2013","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_37","unstructured":"Enobio (2017, April 17). Neuroelectrics. Available online: http:\/\/www.neuroelectrics.com\/products\/enobio."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1161\/01.CIR.93.5.1043","article-title":"Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology","volume":"93","author":"Camm","year":"1996","journal-title":"Circulation"},{"key":"ref_40","first-page":"89","article-title":"Detection of driver\u2019s drowsiness by means of HRV analysis","volume":"38","author":"Vicente","year":"2011","journal-title":"Comput. Cardiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1080\/01621459.1951.10500769","article-title":"The Kolmogorov-Smirnov test for goodness of fit","volume":"46","author":"Massey","year":"1951","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"ref_43","unstructured":"Chouvarda, I., Papadelis, C., Kourtidou-Papadeli, C., Bamidis, P.D., Koufogiannis, D., Bekiaris, E., and Maglaveras, N. (2007). Non-linear analysis for the sleepy drivers problem. Proceedings of the 12th World Congress on Health (Medical) Informatics and Building Sustainable Health Systems, IOS Press. [1st ed.]."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Santamaria, J., and Chiappa, K.H. (1987). The EEG of Drowsiness, Demos Publications.","DOI":"10.1097\/00004691-198710000-00002"},{"key":"ref_45","first-page":"155","article-title":"The Normal EEG of the Waking Adult","volume":"167","author":"Niedermeyer","year":"2005","journal-title":"Electroencephalogr. Basic Princ. Clin. Appl. Relat. Fields"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Picot, A., Charbonnier, S., and Caplier, A. (2008, January 20\u201325). On-line automatic detection of driver drowsiness using a single electroencephalographic channel. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4650053"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1017\/S0048577201393095","article-title":"Driver fatigue: Electroencephalography and psychological assessment","volume":"39","author":"Lal","year":"2002","journal-title":"Psychophysiology"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Awais, M., Badruddin, N., and Drieberg, M. (2014, January 22\u201325). A non-invasive approach to detect drowsiness in a monotonous driving environment. Proceedings of the IEEE TENCON Region 10 Conference, Bangkok, Thailand.","DOI":"10.1109\/TENCON.2014.7022356"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"S\u00f6rnmo, L., and Laguna, P. (2005). Bioelectrical Signal Processing in Cardiac and Neurological Applications, Elsevier Academic Press.","DOI":"10.1016\/B978-012437552-9\/50007-6"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3389\/fphys.2013.00294","article-title":"Heart rate variability in normal and pathological sleep","volume":"4","author":"Tobaldini","year":"2013","journal-title":"Front. Physiol."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"7235","DOI":"10.1016\/j.eswa.2010.12.028","article-title":"Applying neural network analysis on heart rate variability data to assess driver fatigue","volume":"38","author":"Patel","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Soomro, M.H., Badruddin, N., Yusoff, M.Z., and Jatoi, M.A. (2013, January 25\u201328). Automatic eye-blink artifact removal method based on EMD-CCA. Proceedings of the International Conference on Complex Medical Engineering, Beijing, China.","DOI":"10.1109\/ICCME.2013.6548236"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Javed, E., Faye, I., Malik, A.S., and Abdullah, J.M. (2014, January 3\u20136). A Hybrid Method to Improve the Reduction of Ballistocardiogram Artifact from EEG Data. Proceedings of the International Conference on Neural Information Processing, Kuching, Sarawak, Malaysia.","DOI":"10.1007\/978-3-319-12640-1_23"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/1991\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:43:46Z","timestamp":1760208226000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/1991"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,31]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["s17091991"],"URL":"https:\/\/doi.org\/10.3390\/s17091991","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,31]]}}}