AN EEG BASED STUDY OF UNINTENTIONAL SLEEP ONSET MERVYN YEO VEE MIN NATIONAL UNIVERSITY OF SINGAPORE 2007 AN EEG BASED STUDY OF UNINTENTIONAL SLEEP ONSET MERVYN YEO VEE MIN (B.Eng. (Hons.), NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DIVISION OF BIOENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2007 ACKNOWLEDGEMENTS I would like to express my sincere appreciation to my main supervisor, Professor Li Xiaoping for his gracious guidance, advice and sharing of knowledge, expertise and experience, as well as his positive encouragements throughout the PhD study. I thank him for his constant patience and understanding. I wish to thank my co-supervisor, Associate Professor Einar P.V. Wilder-Smith, for his advice, guidance, support and care. I am deeply grateful for his patience, concern and encouragement. His words of encouragement spurred me on during the difficult periods encountered along the course of this study. I would also like to thank my colleagues from the Neurosensors Laboratory in NUS, especially Mr. Zheng Hui, who has helped me in numerous ways during the course of this study. The other colleagues who had helped me in one way or another were Dr. Zhao Zhenjie, Dr. Qian Xinbo, Mr. Oh Tze Beng, Mr. Shen Kaiquan, Mr. Seet Hang Li, Ms. Tan Li Sirh, Mr. Ning Ning, Mr. Fan Jie, Mr. Ng Wu Chun, Miss Pang Yuanyuan, Miss Zhou Wei, and Mr. Cao Cheng. I wish to thank them for their kindness and support. I am also grateful to past undergraduate students whose Final Year Projects were partly involved with this study, particularly Mr. Edmund Ooi Kok Chuan, Mr. Tan Chee Meng, Ms. Christine Ooi Siok Chen and Ms. Yan Ling. This study would not have been successful without their contributions. Finally, I would like to express my sincere thanks to the National University of Singapore and the Division of Bioengineering for their assistance and support during the course of this study. i TABLE OF CONTENTS TITLE PAGE ACKNOWLEDGEMENTS i TABLE OF CONTENTS ii SUMMARY vi LIST OF PUBLICATIONS viii LIST OF TABLES x LIST OF FIGURES xi INTRODUCTION 1.1 Overview 1.2 Objectives of this Study 1.3 Outline of Thesis 1 BACKGROUND AND LITERATURE REVIEW 2.1 Driver Fatigue 6 12 14 14 18 20 2.1.1 2.1.2 2.1.3 2.1.4 The Sleep-Wake Cycle Driver Fatigue and Road Fatalities Caused by Drowsiness Causes of Drowsy Driving Evaluating Sleepiness 2.1.4.1 Objective measures 2.1.4.2 Subjective measures 2.1.4.3 Evaluating driver drowsiness through objective and subjective measures 2.1.5 Technological Countermeasures against Fatigue 2.2 Electroencephalogram (EEG) 2.2.1 2.2.2 2.2.3 2.2.4 Origin of the EEG Signal Electrode Placement for EEG Measurements EEG Signal Characteristics and Classifications EEG and Sleep Architecture – Wakefulness, Drowsiness and Early Sleep 2.2.4.1 Hori’s 9-stage EEG based scoring system 2.2.4.2 EEG graphoelements associated with sleep onset 2.2.5 Detection of Driver Drowsiness by EEG 2.2.5.1 EEG characteristics associated with driver fatigue 2.2.5.2 Drowsiness detection systems developed using EEG 2.2.6 EEG Signal Processing and Data Analysis Methods 25 30 31 31 33 33 35 37 38 40 44 45 ii 2.3 Characteristics of Eye Behaviour as Physiological Markers of Drowsiness 49 2.4 Other Indicators of Drowsiness 57 2.5 Driving Simulators 58 2.6 Support Vector Machines (SVM) 60 62 68 2.6.1 General Principles of SVM 2.6.2 SVM Classification for Multi-class Data 2.7 Summary RESEARCH METHODOLOGY 3.1 Rationale 3.2 Experimental Study Description 3.2.1 3.2.2 3.2.3 3.2.4 Subject Recruitment Equipment Experimental Procedures Experimental Tasks 3.2.4.1 Bed sleeping task 3.2.4.2 Simulated driving task 3.2.5 Data Acquisition 3.3 Experimental Data Analysis Procedures 3.3.1 Overview 3.3.2 EEG Data Pre-processing 3.3.3 EEG Data Processing 3.3.3.1 Synchronization of EEG and video recordings 3.3.3.2 Artefact removal 3.3.4 EEG Data Analysis 3.3.4.1 Visual analysis of EEG data 3.3.4.2 Power spectral analysis of EEG data 3.3.5 Eye Blink and Eyelid Closure Analyses 3.4 EEG Drowsiness Classification by SVM 3.4.1 Feature Extraction 3.4.2 Binary-class Classification of EEG ‘Alertness’ and ‘Drowsiness’ 3.4.3 Multi-class Classification of EEG Data to Establish a Drowsiness Index Specific to EEG Drowsiness 3.5 Summary 70 73 73 75 75 77 80 81 82 82 84 84 84 85 86 86 87 90 91 94 97 98 98 101 105 107 iii RESULTS AND DISCUSSION 4.1 Characteristic EEG Differences between Voluntary Recumbent Sleep Onset in Bed and Involuntary Sleep in a Driving Simulator 4.1.1 Analysis of Sleep Onset Period (SOP) 4.1.2 Differences in EEG Graphoelements 4.1.2.1 Vertex sharp wave morphology 4.1.2.2 Vertex sharp waves and sleep spindles morphology 4.1.3 Discussion 4.1.4 Concluding remarks 4.2 An Automatic Method of Distinguishing between Alert and Drowsy EEG to Improve Driving Safety 4.2.1 Manual Classification of EEG Alertness and Drowsiness 4.2.2 EEG Spectral Characteristics from Alert to Drowsy States 4.2.2.1 Log power spectral analysis 4.2.2.2 Spatial power spectral analysis 4.2.2.3 Discussion 4.2.3 SVM Classification of Alert and Drowsy EEG 4.2.4 Discussion 4.2.5 Concluding Remarks 4.3 A Driver Drowsiness Index (DDI) for the Classification of the Alert-Drowsy-Sleep Transition while Driving 4.3.1 Establishment of DDI by EEG and Video Analysis 4.3.1.1 EEG spectral analysis of DDI 4.3.2 A Detailed Analysis of the Sleep Onset Process under Simulated Driving Conditions 4.3.3 Validation of DDI by Multi-class Classification of EEG Data 4.3.4 Discussion 4.3.5 Concluding Remarks 108 108 108 112 112 116 118 121 122 122 124 125 130 133 134 135 138 139 140 147 149 152 153 156 CONCLUSIONS AND RECOMMENDATIONS 5.1 Conclusions from Study 5.2 Specific Contributions of This Work 5.3 Recommendations for Future Work 157 157 161 161 REFERENCES 163 iv APPENDICES Appendix A: Epworth Sleepiness Scale Appendix B: Sleep Questionnaire Appendix C: Sleep Diary Appendix D: Subject Guidelines Appendix E: Matlab Script for EEG Spectral Analysis Appendix F: Quantification of sleep onset period (in minutes) per subject per experiment Appendix G: Number of VSWs recorded for every experiment per subject from scorers Appendix H: Graphical Representation of the Classification of each subject’s sleep onset periods during day and night driving simulations using the Driver Drowsiness Index (DDI) v SUMMARY Drowsiness while driving is a serious problem and is believed to be a direct and contributing cause of road related accidents, hence endangering lives. If correlates of drowsiness could effectively be used to warn drivers, corrective measures could be taken and disastrous outcomes prevented. The research objective is to improve driving safety by effectively detecting the onset of drowsiness. This is done by (1) identifying the characteristic EEG differences between voluntary recumbent sleep onset and involuntary sleep onset under simulated driving conditions, (2) establishing an automatic method of distinguishing between alert and drowsy states by using Support Vector Machines (SVM), (3) constructing a Driver Drowsiness Index (DDI) to develop a reliable detection system of drowsiness for driving safety. Recumbent sleep tests, day and night driving simulations were conducted on thirty human subjects. Each experiment was conducted on separate days with EEG and video recordings. Alert and drowsy EEG data segments were marked by two raters by visual analysis of EEG, EOG and the identification of eyelid closure events showing 50% of pupil coverage. Samples of EEG data from these segments were used to train binary and multi-class SVM tools by using a distinguishing criterion of four frequency features across four principal frequency bands. The trained SVM program was tested on unclassified EEG and checked for concordance with manual classification. Vertex sharpness during voluntary recumbent sleep onset was significantly sharper. Sharpness of vertices from night-driving was significantly sharper than with day-driving. Triple conjoined vertex waves only occurred with voluntary recumbent sleep onset. A conjoined vertex spindle waveform was statistically associated with vi sleep onset whilst driving. The above results have been published in the journal Clinical Neurophysiology entitled “Characteristic EEG Differences between Voluntary Recumbent Sleep Onset in Bed and Involuntary Sleep Onset in a Driving Simulator”. Manual classification of alert and drowsy EEG data was verified by spectral analysis which revealed significant increases in slow alpha activity (9-11 Hz) along the midline scalp region following drowsiness onset. Binary-class classification between alertness and drowsiness by SVM achieved 99.3% The SVM program was also able to predict the transition from alertness to drowsiness reliably in over 90% of data samples. The above results have been submitted to the journal Safety Science entitled “Can SVM be used for Automatic EEG Detection of Drowsiness to Improve Driving Safety?”. The DDI was established to classify the sleep onset process of drowsy driving into levels. Power spectral analysis of each level showed progressive increases in alpha power with drowsiness. The progression of drowsiness while driving was found to be non-linear unlike normal sleep. The DDI was tested by the SVM multi-class classification tool, achieving 77.2% accuracy. The above results have been submitted to the journal Safety Science entitled “A Driver Drowsiness Index (DDI) for the Classification of the Alert-Drowsy-Sleep Transition while Driving”. Automatic analysis and detection of EEG changes has been achieved by SVM. SVM is a potential candidate for developing pre-emptive automatic drowsiness detection systems for driving safety. This discovery could open many potential applications which require drowsiness detection for safety and performance. vii LIST OF PUBLICATIONS JOURNALS Yeo, M.V.M., X. Li, K. Shen and E. Wilder-Smith. Can SVM be used for automatic EEG detection of drowsiness during car driving? Safety Science, in press. Yeo, M.V.M., X. Li and E. Wilder-Smith. Characteristic EEG Differences between Voluntary Recumbent Sleep Onset in Bed and Involuntary Sleep Onset in a Driving Simulator. Clinical Neurophysiology, 118, pp. 1315-1323, 2007. Yeo, M.V.M., X. Li, K. Shen, H. Zheng, C. Cao and E. Wilder-Smith. EEG Spatial Characterization for Intentional & Unintentional Sleep Onset, Journal of Clinical Neuroscience, 11 sup. 1, pp. 70, 2004. Shen, K., X. Li, C. Cao, M.V.M. Yeo and E. Wilder-Smith. Extraction of Brain Activity Signal from EEG Signals, Journal of Clinical Neuroscience, 11 sup.1, pp. 70, 2004. Yeo, M.V.M., X. Li, K. Shen and E. Wilder-Smith. Can SVM be Used for Automatic EEG Detection of Drowsiness to Improve Driving Safety? Safety Science, in review. Yeo, M.V.M., X. Li, K. Shen and E. Wilder-Smith. A Driver Drowsiness Index (DDI) for the Classification of the Alert-Drowsy-Sleep Transition while Driving. Safety Science, in review. CONFERENCE PROCEEDINGS Yeo, M.V.M., X. Li, E. Wilder-Smith, H. Zheng, K. Shen and L. Yan. Automatic detection of drowsiness to improve driving safety. In Proceedings of International Congress on Biological and Medical Engineering, Singapore, 2005. Yeo, M.V.M., X. Li, Z. Zhao, K. Shen, X.B. Qian, C.M. Tan and K.C. Ooi. Attention Monitoring. In Proceedings of World Congress on Medical Physics & Biomedical Engineering, Sydney, Australia, 2003. Yeo, M.V.M., X. Li, K.C. Ooi, H. Zheng,, L.S. Tan, C.T. Lim, Y.P. Xu, K.Y.T. Lee and E. Wilder-Smith. Intentional and Unintentional Sleep Onset. In Proceedings of International Congress on Biological and Medical Engineering, Singapore, 2002. Yeo, M.V.M., X. Li, K.C. Ooi, H. Zheng,, L.S. Tan, C.T. Lim, Y.P. Xu, K.Y.T. Lee and E. Wilder-Smith. Characterization of intra-stages of sleep onset for driving safety. In Proceedings of International Congress on Biological and Medical Engineering, Singapore, 2002. viii Weisstein, E.W. 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Wierwille, W.W., L.A. Ellsworth, S.S. Wreggit, R.J. Fairbanks and C.L. Kirn. Research on vehicle based driver status: performance monitoring: development, validation, and refinement of algorithms for detection of driver drowsiness. DOT HS 808247, U.S. Department of Transportation, National Highway Traffic Safety Administration, Washington DC, 1994. Wilhelm, H. and B. Wilhelm. Clinical applications of pupillography, Journal of Neuroopthalmology, 23 (1), 42-49, 2003. Wilkinson, R.T. The effect of lack of sleep on visual watch-keeping, Quarterly Journal of Experimental Physiology, 12 36-40, 1960. Wilkinson, R.T. Interaction of lack of sleep with knowledge of results, repeated testing, and individual differences, Journal of Experimental Psychology, 62 (3), 263271, 1961. Wilkinson, R.T. and D. Houghton. Portable four-choice Reaction Time Test with Magnetic Memory, Behavioral Research Methods and Instrumentation, 441, 1975. Williamson, A.M., A.M. Feyer and R. Friswell. The impact of work practices on fatigue in long distance truck drivers, Accident Analysis & Prevention, 28 (6), 709-719, 1996. Wilson, G.F. and J. Lambert. Physiological effects of varied mental workload in pilots during flight, Psychophysiology, 36 S126, 1999. Wilson, B.J. and T.D. Bracewell. Alertness monitor using neural networks for EEG analysis. In Neural Networks for Signal Processing X (ISPS), 2000, 814-820. 187 World Health Statistics. World Health Organisation, Geneva, 1993. Wylie, C.D., T. Shultz, J.C. Miller, M.M. Mitler and R.R. Mackie. Commercial motor vehicle driver fatigue and alertness study: technical summary. FHWA-MC-97001, U.S. Dept of Transportation, Federal Highway Administration, 1996. Yamada, F. Frontal midline theta rhythm and eyeblinking activity during a VDT task and a video game: useful tools for psychophysiology in ergonomics, Ergonomics, 41 678-688, 1998. Yamamoto, S. and S. Matsuoka. Topographic EEG study of visual display terminal (VDT) performance with special reference to frontal midline theta waves, Brain Topography, 257-267, 1990. Yasoshima, A., H. Hayashi, S. Iijima, Y. Sugita, Y. Teshima, T. Shimizu and Y. Hishikawa. Potential distribution of vertex sharp wave and sawtoothed wave on the scalp, Electroencephalography & Clinical Neurophysiology, 58 73–76, 1984. Yeo, M.V.M., X. Li and E.P.V. Wilder-Smith. Characteristic EEG differences between voluntary recumbent sleep onset in bed and involuntary sleep onset in a driving simulator, Clinical Neurophysiology, 118 1315-1323, 2007. Yeo, M.V.M., X. Li, K. Shen and E. Wilder-Smith. Can SVM be used for automatic EEG detection of drowsiness during car driving? Safety Science, in press. Zomer, J. and P. Lavie. Sleep-related automobile accidents - when and who? In Sleep ‘90, ed by J.A. Horne, Bochum: Pontenagel Press. 1990. 188 Appendix A The Epworth Sleepiness Scale Questionnaire administered as a pre-screening tool to potential subjects. THE EPWORTH SLEEPINESS SCALE How likely are you to doze off or fall asleep in the following situations, in contrast to feeling just tired? This refers to your usual way of life in recent times. Even if you have not done some of these things recently, try to work out how they would have affected you. Use the following scale to choose the most appropriate number for each situation: = would never doze = slight chance of dozing = moderate chance of dozing = high chance of dozing Situation Sitting and reading Watching TV Sitting, inactive in a public place (e.g. a theatre or a meeting) As a passenger in a car for an hour without a break Lying down to rest in the afternoon when the circumstances permit Sitting and talking to someone Sitting quietly after a lunch without alcohol In a car, while stopped for a few minutes in traffic Chance of dozing Thank you for your cooperation. The Epworth Sleepiness Scoring Criteria for assessing the sleepiness of potential subjects. Slight sleepiness 0-5 Moderate sleepiness 6-10 Heavy sleepiness 11-20 Extreme sleepiness 21+ A1 Appendix B Sleep Questionnaire (administered online) You are required to answer the following questionnaire to assess your suitability for this study. Name: _____________________________ NRIC: _____________________________ Contact: _________________(HP) _________________(email) Age: Sex: Race: Occupation: Do you have any chronic illnesses? If yes, please specify _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Are you on any long term medications? If yes, please specify _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Are you currently under any medication? If yes, please specify _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Do you have any medical history? (e.g. diabetes, high cholesterol, high blood pressure, hepatitis B, etc.) If yes, please specify _____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ Do you suffer from any of the following problems? If yes, please indicate: 1. 2. 3. 4. 5. Sleep Apnea Narcolepsy Periodic Leg Movements Excessive Daytime Sleepiness Insomnia B1 Appendix B (continued) A. Sleeping habits during the past one month Instructions: The following questions relate to your usual sleep habits during the past month only. Your answers should indicate the most accurate reply for the majority of days and nights in the past month. Please answer all questions. During the past month, 1. When have you usually gone to bed? 2. How long (in minutes) has it taken for you to fall asleep each night? 3. When have you usually gotten up in the morning? 4. How many hours of actual sleep did you get? (This may be different from the number of hours you spend in bed) Not during Less than once a the past month month Three or Once or twice a more times week a week 1. During the past month, how often have you had trouble staying awake while driving, eating meals, or engaging in social activity? 2. During the past month, how much of a problem has it been for you to keep up enthusiasm to get things done? Very good Fairly good Fairly bad Very bad 3. During the past month, how would you rate your sleep quality overall? B. Response to sleep loss 1. Which of the following best describes you (select one only): If I sleep later than usual, I have no problems sleeping more and waking up later than usual If I sleep later than usual, I still wake up at the same time in the morning 2. Which of the following best describes you (select one only): I find it easy to recover from sleep loss I find it difficult to recover from sleep loss B2 Appendix B (continued) C. Sleep Duration 1. On a working/ school day, what is your usual sleep duration bedtime waking time 2. On a non-working/ non-school day or weekend, what is your usual sleep duration bedtime waking time 3. Regarding your sleep last night sleep duration bedtime waking time Thank you for your responses. All information will be kept strictly confidential. We will be contacting you if you are selected for this study. B3 Appendix C Sleep Diary Form (to be filled in by subjects one week before experiments) Name: Date of Experiment: Date Sleep Record Woke up at Slept at Time and Duration of nap (if any) Woke up at Slept at Time and Duration of nap (if any) Woke up at Slept at Time and Duration of nap (if any) Woke up at Slept at Time and Duration of nap (if any) Woke up at Slept at Time and Duration of nap (if any) Woke up at Slept at Time and Duration of nap (if any) Woke up at Slept at Time and Duration of nap (if any) C1 Appendix D Subject Guidelines Dear (Subject’s name), Thank you for participating in a sleep onset evaluation study organized by the Neurosensors Laboratories, NUS. Please read the instructions as listed below. Background Information Feeling sleepy is especially dangerous when you are on the road. Sleepiness slows your reaction time, decreases awareness and impairs your judgment. The purpose of this study is to help us classify the stages of unintentional sleep onset in drowsy drivers, thus enabling us to identify the critical point of drowsiness. This study consists of driving simulations and a bed-sleeping session. Each driving simulation will last approximately hour. Throughout the experiment, a safe and highly sophisticated technology known as EEG will be used to monitor your brain activity. Safe and non-invasive electrodes will be placed on your head to record EEG signals during experimentation. One of the driving simulations, the day driving simulation, will be carried out at 3pm. A night driving simulation will be carried out at 12 midnight on a separate day. The bed-sleeping session will be conducted at 4pm on a separate day and will last for 30 minutes. A video camera would be used to film you, particularly your facial and body movements necessary for our analysis. Subject Etiquettes 1. You are required to co-operate with the investigator at all times of the study. Any failure to so may result in your disqualification from the study. 2. Note that punctuality must be observed at all times of the study. Any failure to observe punctuality may result in your disqualification from the study. 3. You are required to adhere to the housing schedule upon confirmation of your availability. Last-minute requests for changes in schedule will not be entertained. 4. You are to go to bed no later than 1am and wake up before am the following day for days before each experiment. This is necessary to ensure that your sleeping hours are regular. 5. You are required to wake up at 7.00 am and have at least hours of sleep on the day of each experiment to ensure consistency in waking times across experiments. D1 6. You are required to report to the laboratory at least one hour before the scheduled experimenting time for pre-experimental preparation. The reporting times for each experiment are stated below: Day driving simulation Night driving simulation Bed -sleeping session Time of Experiment 3:00pm 12:00am 3:00pm Reporting time 2:00pm 11:00am 2:00pm 7. Please avoid fruit juices and caffeinated drinks such as coffee and tea on the day of experiment. 8. Please refrain from using hair gel or hair lotion on the day of the experiment to facilitate the attachment of EEG electrodes. How can I benefit? Your volunteering contributes to the understanding of brain activity in drowsy drivers. You will receive a fee of S$75 in completion of this study. Confidentiality All information gathered in the course of this study will be strictly confidential and would only be used for the purpose of this study. Thank you for your co-operation. D2 Appendix E Matlab Script for EEG Spectral Analysis NumCh=19; %No. of channels Window=334; %Window length (Hamming) Overlap=0; %Default 50% overlap NFFT=334; %No. of FFT pts to calculate PSD Fs=167; %Sampling frequency %Frequency window of spectrum StartFreq=2; %Set start freq EndFreq=25; %Set end freq P = zeros(NFFT/2+1,NumCh); for i=1:NumCh; [P(:,i),F]=pwelch(EEG.data(i,:),Window,Overlap,NFFT,Fs); %FFT end M = mean(P',1); %Global mean M = log10(M); P = log10(P); %Log pw of each ch StartFreqPts=StartFreq/0.5+1; EndFreqPts=EndFreq/0.5+1; F=F(StartFreqPts:EndFreqPts); M=M(StartFreqPts:EndFreqPts)'; P=P(StartFreqPts:EndFreqPts,1:NumCh); figure; plot(F,M); figure; plot(F,P); %Normalizing Global Mean Power(M) Mm = mean(M); Md = std(M); Mmed = median(M); Ms = sort(M); MQA = median(Ms(find(Msmedian(Ms)))); %75 percentile MIQR = MQB-MQA; % compute Interquartile Range (MIQR) MSID = MIQR/2; % compute Semi Interquartile Deviation (MSID) Mn = (M-Mm)/MSID; %normalized power %Normalizing Channel Power(P) for 28 channels for j=1:NumCh; Pm(j) = mean(P(:,j)); Pd(j) = std(P(:,j)); Pmed(j) = median(P(:,j)); S(:,j) = sort(P(:,j)); PQA(j) = median(S((find(S(:,j)Pmed(j))),j)); %75 percentile % compute Interquartile Range (IQR) PIQR(j) = PQB(j)-PQA(j); % compute Semi Interquartile Deviation (SID) PSID(j) = PIQR(j)/2; Pn(:,j) = (P(:,j)-Pm(j))/PSID(j); %normalized power end figure; plot(F,Pn); end E1 Appendix F Quantification of sleep stage episodes experienced by each subject under the respective experimental conditions. (Legend: SD = standard deviation, SS1 = sleep stage 1, ET = experiment time) Subject Mean latency (min) 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Mean SD 2 2 3 2 1 3 3 2 2 3 2 2.1 0.7 Recumbent Sleep (ET: 30min) Total SS1 % of total No. of time (min) SS1 to ET SS1 (%) events 15 10 2 16 22 17 20 22 12 20 22 25 12 16 25 18 21 20 19 24 22 21 14.8 7.6 50.0 33.3 6.7 6.7 23.3 53.3 73.3 56.7 16.7 23.3 66.7 73.3 40.0 23.3 10.0 26.7 66.7 10.0 73.3 83.3 40.0 53.3 83.3 60.0 70.0 66.7 63.3 80.0 73.3 70.0 49.2 25.2 1 1 1 1 2 1 1 3 1 3 3 3 1.8 0.9 Mean latency (min) 4 4 4 1 1 4 2.8 1.4 Day Driving (ET: 60min) Total SS1 % of total No. of SS1 time (min) SS1 to ET events (%) 13 12 10 1 14 1 15 9 1 13 11 6.1 4.7 15.0 8.3 1.7 5.0 21.7 15.0 20.0 11.7 16.7 1.7 1.7 1.7 23.3 1.7 1.7 25.0 5.0 1.7 11.7 13.3 3.3 15.0 15.0 1.7 1.7 1.7 21.7 10.0 11.7 18.3 10.1 7.9 1 1 5 1 2.9 1.8 Mean latency (min) 1 1 1 5 1 5 1 3.2 2.5 Night Driving (ET: 60 min) Total SS1 % of total No. of SS1 time (min) SS1 to ET events (%) 12 14 5 16 13 18 10 1 13 12 15 13 5 6.8 5.3 1.7 20.0 23.3 8.3 8.3 1.7 26.7 8.3 21.7 30.0 5.0 6.7 10.0 16.7 1.7 1.7 21.7 20.0 1.7 25.0 3.3 21.7 6.7 1.7 8.3 10.0 8.3 6.7 3.3 8.3 11.3 8.8 3 2 1 5 2 3.4 2.3 F1 Appendix G Number of VSWs recorded for every experiment per subject from scorers. Subject Intentional Sleep st nd Day Driving st Night Driving nd st score score score score score 2nd score 25 18 23 22 21 13 13 12 20 18 66 54 40 46 15 16 36 40 11 60 50 50 55 31 32 14 10 10 19 15 11 20 12 25 20 12 12 10 21 19 20 15 13 45 49 36 32 11 13 14 15 16 36 28 25 23 17 18 19 16 10 20 21 22 35 28 20 15 25 18 23 25 19 24 65 55 25 12 26 18 27 45 36 32 33 28 18 13 15 29 39 30 30 17 12 Mean 21.7 19.3 10.8 9.4 7.4 4.8 SD 21.5 19.7 11.6 10.9 8.3 6.3 G1 Appendix H Graphical Representation of the Classification of each subject’s sleep onset periods during day and night driving simulations using the Driver Drowsiness Index (DDI) Legend: Level Level Level Level Level Subject Day driving simulation session (60 min) 60 10 11 12 13 14 15 H1 Level Level Level Level Level Subject Day driving simulation session (60 min) 60 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 H2 Level Level Level Level Level Subject Night driving simulation session (60 min) 60 10 11 12 13 14 15 H3 Level Level Level Level Level Subject Night driving simulation session (60 min) 60 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 H4 [...]... and wakefulness Sleepiness results from the sleep component of the circadian cycle of sleep and wakefulness, restriction of sleep, and/or interruption or fragmentation of sleep The loss of one night’s sleep can lead to acute sleepiness, while habitually restricting sleep by 1 or 2 hours a night can lead to chronic sleepiness Sleeping is the most effective way to reduce sleepiness The sleep- wake cycle... a broad summary of EEG, such as electrode placement, commonly referenced EEG frequencies associated with wake and sleep, and sleep architecture In addition, a literature review is given pertaining to the EEG and the detection of drowsiness, the roles of the classical EEG frequency bands, and an overview of various signal processing and data analysis methods used in the analysis of EEG signals from... the onset of sleep so that the driver can be given sufficient warning time to react properly and take steps to avoid incipient sleep 1.3 Outline of thesis As an introduction, chapter 1 examines the role of fatigue and extreme sleepiness in automobile accidents and the enormous costs (both monetary and human) associated with them The objectives of the current study are outlined and an overview of the... daytime sleepiness and is also a measure of instantaneous sleep propensity This test is based on the assumption that the faster an individual falls asleep, the sleepier he/she must have been prior to sleeping The speed with which the individual falls asleep, sleep latency, can be used to evaluate the propensity of falling asleep, i.e sleepiness The test is performed in a laboratory setting and under standardized... to analyze EEG parameters can be summarized in patterns and spectral characteristics Spectral analysis of a sleep EEG often involves power density of alpha and theta activity Although the interpretation of EEG signals has been standardized in research literature, there is presently no standardized way to evaluate driving sleepiness using the EEG technique (Van den Berg, 2006) 14 Multiple sleep latency... rather than the result of sleep abnormalities (Horne and Reyner, 1997) 2.1.3 Causes of Drowsy Driving Fatigue and drowsiness are conditions that impair drivers’ information processing thus increasing the likelihood of various perceptual and attention errors (Davies and Parasuraman, 1982) The primary causes of sleepiness and drowsy driving in people without sleep disorders are sleep restriction and sleep. .. Zheng,, L.S Tan, C.T Lim, Y.P Xu, K.Y.T Lee and E Wilder-Smith Critical Electrode Positions to EEG Measurement and Monitoring of Sleep Onset In Proceedings of International Congress on Biological and Medical Engineering, Singapore, 2002 ix LIST OF TABLES Table 3.1: Scheduling of subjects to ensure order of experimental sessions is counterbalanced Table 3.2: EEG signal characteristics of the NREM sleep stages... Economic pressures and the global economy place increased demands on many people to work instead of sleep, and work hours and demands are a major cause of sleep loss Often, however, reasons for sleep restriction represent a lifestyle choice – sleeping less to have more time to work, study, socialize, or engage in other activities Although the need for sleep varies among individuals, sleeping 8 hours... driving, light sleep and its typical characteristics may ensue either intermittently or completely when the struggle to stay awake has ceased Despite these instances of sleep shifting from involuntary mode to voluntary mode, the different sleeping postures between recumbent sleep and sleep whilst driving are also an important area of study 2 1.2 Objectives of this Study The purpose of this research... voluntary sleep onset under bed-sleeping condition in a reclined position and involuntary sleep onset under simulated driving conditions in a sitting position To achieve the above, an investigation of EEG differences between day and night driving which corresponds with the two sleepiness peaks in a daily circadian cycle is included as part of this study, as the time -of- day differences between these circadian . the EEG and the detection of drowsiness, the roles of the classical EEG frequency bands, and an overview of various signal processing and data analysis methods used in the analysis of EEG. AN EEG BASED STUDY OF UNINTENTIONAL SLEEP ONSET MERVYN YEO VEE MIN NATIONAL UNIVERSITY OF SINGAPORE 2007 AN EEG BASED STUDY. This endangers the lives of the driver and passengers, and causes serious accidents along major roads (Lal and Craig, 2001). 1 For this reason, study of the EEG characteristics of sleep onset