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Batch processing of 10000 h of truck driver EEG data
BIOLOGICAL PSYCHOLOGY ELSEVIER
Biological Psychology 40 (1995) 209-222
Batch processing of 10 000 h of truck driver EEG data James C. Miller Miller Ergonomics, 8915 Rocket Ridge Rd. Lakeside. California 92040-4924, USA
Accepted 3 November 1994
Abstract This paper describes the methods used to acquire and reduce a massive amount of EEG data (Wylie et al., 1990). The description is introduced by a review of a previous effort
(Mackie and Miller, 1978). The earlier effort created much of the design philosophy for the second effort. The majority of data in the Paradox database came from 400 trips contributed by 80 commercial drivers driving both day and night revenue-cargo runs of 10 or 13 h each. The sleep and driving EEG data were collected with ambulatory Medilog recorders. Breathing and oxyhemoglobin measures were collected during sleep for sleep-apnea determinations. The sleep and driving-EEG data were placed in raw digitized files (128 samples/second), with the latter also available as compressed-band arrays for 20-s epochs, with associated Rechtschaffen and Kales (1968) manual scoring by polysomnographers for all EEG data. Sleep EEG, subjective driving performance and discrete-task data were also placed in the database, integrated and time-registered to within l-s accuracy with the driving EEG data. Each truck was extensively instrumented for lateral lane position, steering wheel position, speed, video image of the roadway, and video image of the face. Each driver recorded body temperatures several times per day, provided Stanford Sleepiness Scale readings several times each day, and was connected to the Vagal Tone Monitor while driving. In addition, driving segments were prefaced and followed by the performance of the Critical Tracking Task, the Psychomotor Vigilance Task, and the Code Substitution Task. The database should serve as an international resource from which many investigators may draw data. Keywords:
Methods;
* Corresponding userve.com.
EEG; FFT; Trucking; Sleep
author, Tel.: (619) 443-4427; Fax: (619) 443-9347; Internet: 72143.2143@comp-
0301-051l/95/$09.50 0 1995 Elsevier Science Ireland Ltd. All rights reserved SSDI 0301-0511(95)05114-P
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1. Introduction There is recognition of the fact that commercial truck drivers, because of irregularly-scheduled work and night work, suffer on-the-job fatigue. Approximately 30% of fatal-to-the-driver commercial vehicle accidents involve driver fatigue as a primary cause (National Transportation Safety Board, 1990). With fatigue comes the increased likelihood of periods during which we fail to respond to important occurrences in the work environment. These periods may be brief ‘lapses’ (e.g., Wilkinson et al., 1982; Williams et al., 1959) or they may be longer ‘microsleeps’ (e.g., O’Hanlon and Kelley, 1977). There are two portions of the 24-h day during which errors, due probably to lapses and microsleeps, are most prevalent in many occupations (Bjerner et al., 1955; Browne, 1949; Mitler, 1989; Prokop and Prokop, 1955). These periods include the pre-dawn hours and the mid-afternoon hours. During a lapse or a microsleep, we are able to successfully carry on highly-learned, automated behaviours in unremarkable environments. Most of us have experienced an occurrence of a lapse or microsleep while driving. We suddenly realize that we cannot recall any information about the last mile or two driven on the highway. Brown (1994) characterized these occurrences as periods when attention drifts from the task at hand to an internal focus. Objective measures of microsleeps were captured during open-highway driving during an experiment using drowsy drivers in a van equipped with a recording device for brain electrical data and carrying an on-board safety observer with steering and brake controls (O’Hanlon and Kelley, 1977). Drivers’ brain waves indicated a sleep state for up to 15 s while they continued to drive within their lane on a straight segment of freeway. They tended to weave within the lane, but did not crash. Eventually, the drivers may have drifted off the roadway during longer microsleeps had they not been alerted by the safety observer, who assumed control of the vehicle. A number of theories for this ubiquitous phenomenon have been proposed. The theories are based upon data linking human performance to psychophysiological arousal and to behavioral inhibition, reinforcement, filtering, expectancy, and signal detection (Miller and Mackie, 1980). The primary factors which affect the performance of humans required to remain vigilant include the absolute and relative rates at which important and unimportant occurrences occur in the environment; the complexity, timing, and conspicuity of the occurrences; the sense involved (vision, hearing); the time of day; the total work load placed on the individual; the work schedule and its interactions with sleep quality and with bimodal circadian rhythms in human performance; the individual’s motivation; attention from management; and others (Miller and Mackie, 1980). The two investigations described here have focused on psychophysiological aspects of fatigue and sleepiness, and have attempted to control, to the degree possible in field research, several of the factors cited. Several characteristics of the spontaneous EEG are of interest in the investigation of driver fatigue and sleepiness. The disappearance of the alpha rhythm from the awake EEG upon direction of attention to sensory stimuli, voluntary movement, or problem solving was described by Berger (1929) and later termed ‘alpha blocking’ by Adrian (1944). The identification of the midbrain reticular formation as the subcortical structure primarily involved in alpha blocking and behavioral arousal was
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pioneered by Moruzzi and Magoun (1949). Diffuse projections of the mid-line thalamic nuclei were identified as mediating the cortical alpha rhythm (Jasper, 1960), and the alpha rhythm itself appeared to be a result of synchronously occurring inhibitory and excitatory post-synaptic potentials in cortical neurons (Li and Jasper, 1953). Drowsiness may ensue following the appearance of the alpha rhythm if the relaxed subject is not disturbed. In this case the alpha rhythm gives way to some amount of theta activity, additional low-voltage, mixed-frequency EEG, and slow eye movements (Schacter, 1977). Phasic occurrences of sigma waves and large, biphasic Kcomplexes normally signal the onset of sleep, followed by the increasing dominance of delta waves, defining deeper sleep (Rechtschaffen and Kales, 1968; Roth, 1961). This paper describes the methods being used to acquire and reduce a massive amount of EEG data (Wylie et al., 1990). The methods described reflect experience gained in an over-the-road investigation of driver fatigue (Mackie and Miller, 1978) actual flight (Gawron et al., 1987, 1988) and flight simulation (Morris and Miller, 1983), ship simulation (O’Hanlon et al., 1975; Miller, 1976), centrifuge (Lewis et al., 1987) and altitude chamber investigations (Miller and Horvath, 1977a, 1977b), and laboratory investigations (Miller, 1982; Miller et al., 1985). The description is introduced by a review of a previous effort (Mackie and Miller, 1978). This earlier effort created much of the design philosophy for the present effort. 1.1. The 1978 fatigue study.
This project was an investigation of driver fatigue and loss of alertness as possible contributors to accidents experienced by interstate motor carriers of property or passengers (Mackie and Miller, 1978; also see Mackie and Miller, 1981; Miller, 1988; Miller and Mackie, 1978). Four questions were addressed. Is the amount of fatigue experienced by commercial drivers affected (1) by work periods that are irregular with respect to the day-night cycle? (2) by taking their rest periods solely within their vehicle while another person continues driving (sleeper operations)? (3) by performing physical work in addition to driving? (4) by engaging in such operations over an extended number of days? (Mackie and Miller, 1978). In addition to a literature review, a survey, and an accident risk analysis (Harris, 1977), data were collected during three over-the-road investigations using two instrumented commercial vehicles, a 1970 Mack air-conditioned cab-over-engine tractor with sleeper berth plus a 40-ft (12.3 m) semi-trailer with 37 000 lb (16 800 kg) ballast, and a 1958 GMC model PD4104, 35-ft (10.6 m) air-conditioned passenger bus with no ballast. The three data collection conditions were Relay (single) Truck Drivers, Sleeper (paired) Truck Drivers, and Bus Drivers. There were six experienced drivers in each condition. Each Relay driver experienced three different weeks of operations: regular schedule with light physical work, irregular schedule with light physical work, and irregular schedule with moderate physical work. Each Sleeper driver experienced the latter two weeks (their schedules were all irregular). Each Bus driver experienced the first two conditions (light physical work). The EEG was recorded only during driving, i.e., not during sleep. Each Relay
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driver drove about 150.5 h for 3 weeks, each Sleeper driver about 80 h for 2 weeks, and each Bus driver about 90 h for 2 weeks, during operations. The lost data rates for EEG (after editing, described below) were about 12.6%, 13.5%, and 8.6%, respectively. This produced a total product of about 1923 h and a net product of about 1698 h of driving EEG data. The montage was a single channel, Oi-P3 (Jasper, 1958). A custom-made, bipolar preamplifier (lo4 gain, 80 db common-mode rejection, 0.3-s time constant) was placed near the electrodes, and the data were recorded in pulse-width modulation format. Data from the first 18 s of each minute were digitized as 2056 samples (about 114 samples/second), allowing a 0.055 Hz fundamental frequency and bin width for frequency analysis. Epochs with values below 5 PV p-p throughout the 18-s epoch, and peaks within epochs above 100 PV p-p were ignored automatically. The EEG epochs were zero-suppressed and tapered, then subjected to a fast Fourier transform (FFT). Spectral relative power estimates were aggregated into theta (5.005-7.975 Hz), alpha (8.030-12.045 Hz) and beta (16.005-30.030 Hz) bands. Delta-band data were not used due to expected eye movement artifact. Band-specific relative power data were interactively edited by highway segment within each trip. Each variable was displayed in a graphic scatterplot which included a linear regression line for the highway segment. Regression line slopes and mid-points were stored as reduced data representing each highway segment. The regression line mid-points represented the average band-specific energy value for the highway segments of about 15 min to 1 h in length. Since the experimental highway route was an out-and-back arrangement, data from the inbound highway segments, late in each trip, were compared to data from the outbound segments, early in each trip, by analysis of variance. Since changes in alpha activity tend to be ambiguous, we focused on theta and beta activities in the analysis. (By ambiguous, I refer to the alpha-blocking triggered both by alerting and by the onset of Stage 1 drowsiness). Statistically significant and nearly significant differences (p c 0.05 or p c 0.10, respectively) occurred in nearly all trips in at least one of the two frequency bands, relative theta and beta. The effect sizes were generally in the range of 2-10% increases or decreases. The effects were generally interpretable as ‘arousal’ (increased beta and/or decreased theta) or ‘fatigue’ (decreased beta and/or increased theta). Looking back at the data, there were a couple of interesting, specific occurrences. First, during Relay operations, relative theta activity was higher (p < 0.05) during two night driving segments on Interstate Highway 5 (I-5) than at other times on narrower highways (Mackie and Miller, 1978, Fig. 59). Lane tracking was not impaired for these two segments (Fig. 56). Second, during Sleeper operations, relative theta activity was higher (p c 0.05 and p < 0.10) for a OO:OO-05:OO h run across several highway types, compared to other times of day (Fig. 78). Lane tracking was not impaired for that run (Fig. 75). Bus operations were not demanding enough to fatigue the drivers to the point of elevated theta activity. These observations suggested that, as expected, drowsiness occurred often during the pre-dawn hours. They also indicated that lane-tracking performance was not grossly disturbed during that period. This pattern may have reflected mild
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drowsiness and reduced vigilance accompanied by highly automated lane-tracking behavior. In this situation, a subtle system change, like a gradual drift to the edge of the road, may not be noticed. Compressed spectral array plots of sequential data epochs showed waxing and waning of relative theta power during driving (Fig. 1). It appeared from plots such as these that, in a future investigation, a theta:beta ratio might provide a reasonably good physiological indicator of possible drowsiness episodes. Three of the conclusions we drew concerning truck Relay and Sleeper operations included evidence gained from EEG data. Firstly, some cumulative fatigue occurs during 6 consecutive days of relay operations. Evidence included low cortical arousal on the first day and high cortical activation on the sixth day of operations for drivers on the regular schedule. The philosophy here was that greater cortical activation reflected the effort to remain awake (Cameron, 1973). Secondly, participation in moderately heavy cargo-loading to the extent engaged in by many relay truck drivers increases the severity of fatigue associated with irregular working schedules. Evidence included less theta and more beta in the EEG in the moderate work condition suggesting that, despite any possible benefits of physical work, greater cortical activation was required to maintain performance. Finally, Sleeper-driver fatigue, physiological state, and performance are strongly affected by time of day. Evidence included significant elevations in cortical activation (increased beta, decreased theta) during late night or early morning trips, indicating increased effort to remain alert. These conclusions focussed strongly on mean values of relative EEG band activity from highway segments ranging from 15 min to 1 h long. They did not take into account the occurrence and potential effects of brief microsleeps, nor did we detect and calculate the frequency of microsleeps. There were two other, more obvious limitations to our EEG acquisition and analysis process. Firstly, we were technically limited by the computer network and the time required to digitize the raw data. Because of the limitations, we chose to look at only 18 s of EEG data from each minute. We rationalized that EEG manifestations of arousal probably wax and wane across periods of several minutes, and that we would not miss these variations if we sampled regularly from each minute. However, we learned that an apparent microsleep can be completed during about 15 s (O’Hanlon and Kelley, 1977). Secondly, we did not record the sleep of the drivers. We had the expertise and equipment available to implement full Rechtschaffen and Kales (1968) assessments. However, limits in contract scope precluded taking that investigative path. In the present investigation, described below, we avoided these two limitations. Even though the investigation had these limitations, it provided a strong impetus for us to acquire EEG data during subsequent investigations of operator fatigue. In addition, during the 198Os,the concept of the sleep disorders clinic was realized in most major medical facilities. This occurrence brought formal organization and accreditation to clinical polysomnographic methods and personnel. This clinical use of the EEG helped establish the EEG as a widely accepted indicator of the state of human consciousness and the quality of sleep acquired during rest periods.
J. C. Miller
Table I Experimental
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design
Schedule
Drive time per duty period
Day driving Night driving
10 h
13 h
n = 20 n = 20
n = 20 n = 20
1.2. The present fatigue study.
The present investigation was designed (Wylie et al., 1990) to answer the following questions (paraphrased): (1) do commercial vehicle drivers experience operationally significant losses of alertness due to fatigue within current daily and weekly hours of service drive-time limits? (2) if so, after how many hours of driving do these debilitating effects occur? (3) does loss of alertness due to fatigue occur earlier, or is it more severe, on irregular vs. regular schedules? (4) do older drivers (> 45 years) experience an earlier or more severe loss of alertness than younger drivers? (5) does quality or quantity of sleep influence the latency or severity of loss of alertness? (6) is there cumulative fatigue across days? (7) what practical countermeasures might be used to combat loss of alertness due to fatigue? 2. Methods 2.1. Experimental design and procedures Twenty male, commercial drivers participated in cells of a 2 x 2 matrix of experimental conditions, as shown in Table 1. The 10-h driving per duty period occurred on runs between St. Louis and Kansas City. The 13-h driving per duty period occurred on runs between Toronto and Montreal. All trips carried revenue-producing Table 2 Planned hours of data acquisition
by experimental
Condition
Trips
Hours
10-h 13-h Recovery Regional
200 200 55 100
8 8 8 8
Total
condition
sleep
Hours IO I3 I3 5
driving
Total hours 3600 4200 II55 I300 10 255
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cargo for the carriers who assisted us (Yellow Freight, Consolidated Freightways, and in Canada, SLH, a Sears subsidiary). Ten drivers in each cell fell in the 21-45 year age range and the other 10 were over 45 years. Each driver operated for 1 week in this design, producing five trips, for a total of 400 trips. Each driver provided informed consent to participate as a subject in the investigation. There was also a Transport Canada-sponsored add-on investigation of 55 trips, designed to allow an examination of recovery functions following daytime runs between Toronto and Montreal. A regional operations cell with about 100 trips was also planned, but not carried out. A 3-digit trip number described the cycle, sleep-drive-drive, where sleep was the major sleep period, the first drive was the outbound leg (5-6.5 h), and the second drive was the inbound leg. The fifth trip in the 13-h conditions contained only a sleep period and no driving periods. The EEG was recorded during sleep (nominally 8 h) and driving (nominally 10 or 13 h). We planned 10 255 h of EEG acquisition, as shown in Table 2. In fact, the drivers slept much less than 8 h per shift. In addition, the 13-h condition allowed only four drives instead of five drives per week (however, five sleep periods were measured). Thus, we concluded data collection with about 7500 h of EEG data. 2.2. Signal acquisition The recording montages are shown in Table 3. The electrode placements (channels l-5) allowed signal acquisition that supported the standardized polysomnographgic scoring method (Rechtschaffen and Kales, 1968). During sleep, channels 5-7, in conjunction with fingertip oximetry, allowed assessments of sleep apnea. The Oxford Medilog 9000 tape system (Oxford Instruments Ltd, Abingdon, Oxon, England) was chosen because of its track record for acquiring data reliably. Table 3 Electrode and sensor placements for sleep and driving Channel 1 2 3 4 5 6 7 8 Indifferent
Sleep
Driving
C4-AI
C4-A
01742
01 -A2
LOC-ROC
LOC-A, ROC-A,
C3-A2
EMG-EMG (chin) Chest motion Oral/nasal airflow ECG cz (two)
I
C3-A2
Cz (two)
The central (C), auricular (A) and occiptial(0) electrode sites were consistent with the International IO-20 System (Jasper, (1958). The left-outer-canthus-right-outer-canthus (LOC-ROC) derivation was described, for example, by O&ton (1974).
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Data acquisition was the most critical aspect of our data-handling task. The Medilog has a long history of reliability and had performed well recently for the NASA-Ames Research Center Human Factors Division during its cockpit-napping studies for the FAA (Rosekind et al., 1991). Concomitantly, we used the Oxford 9200 datareduction system to decode the tapes and digitize the EEG data. 2.3. Data reduction For administrative purposes only, the EEG tapes were auto-scored at 20 x real time on the 9200 (Oxford software version 8.7). The sleep-stage results of the autoscoring process were ignored. However, the auto-score process set up the segment start and stop times and then allowed the segment to be digitized at 60 x real time and stored on the computer’s hard disk. Subsequently, the digitized file was transferred to a removable optical disk in the 9200. Files were sent to the Sleep Disorders Division of the Scripps Clinic and Research Foundation (SCRF) from the field on approximately forty-five 960-megabyte, write once, read many (WORM) optical disks, providing some protection against overwriting valuable data. At SCRF, polysomnographers copied the data from WORM disks back to a second 9200’s hard disk. They manually scored sleep- and drivingsegment EEGs using the graphics and data-logging capabilities of the 9200 and Rechtschaffen and Kales (1968) standards. For the major sleep period and for naps, the epoch length was 30 s. We combined the manual scoring output of Oxford 9200 software with the oximetry output of Profox oximetry software (Profox Associates, Inc., version 12s). Thus, each 30-s epoch was human-coded for sleep stage and oximetry-coded, as well. The two files were synchronized to 1 min accuracy by the field technicians’ matching of the oximeter time to the Medilog recorder time. If a driver was found to have clinically significant sleep apnea, SCRF specialists in apnea scored all of that driver’s sleep records. The allowable EEG scores were (Rechtschaffen and Kales, 1968): M, motion artifact (unusable signal); W, awake (usable signal); 1, stage 1; 2, stage 2; 3, stage 3; 4, stage 4; R, stage REM. The resulting reduced data were structured as one record per night of sleep. One Paradox (Borland International, Inc., Scotts Valley, CA) database file was created to hold the many nights of sleep data. The fields of each record are listed in Appendix I. These data were integrated into the database with driving data recorded from the driver and from the instrumented trucks. For driving periods, the epoch length was 20 s. We focused our manual scoring on the Cs-A2 channel or on the &-A, channel, using the cleaner one. We combined the frequency-analysis output of Rhythm (Stellate Systems, Westmount, Quebec, Canada, versions 7.1 and 8.0) frequency-analysis software with the manual scoring output of Oxford 9200 software. Thus, each 20-s epoch was human-coded as shown above. The two files were synchronized by virtue of the former serving as the source for the latter. To use the Rhythm frequency-analysis software, we converted a digitized EEG data file from the Oxford system using an Oxford-provided conversion program
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J. C. Miller / Biological Psychology 40 (1995). 209-222 Theta 8000
Power, No “M”
, i
7000 6000 (y 5000
(
0
20
40
60
80
100 120 140
Minutes
Fig. 2. Spectral estimates of theta band activity as a function of drive time.
(crhythm.exe version 2.1 from Oxford). The conversion from Oxford to Stellate file format took about 3 min for a 20-megabyte EEG file. The Stellate file format held digitized data, sampled at 128 sample&c, in 4-s (512-sample) epochs. These were concatenated into 20-s epochs for processing only (the original data remained in digitized 4-s epochs). Each 20-s epoch was zero-suppressed, tapered with a 40% cosine function, then subjected to a fast Fourier transform (FFT) with the Rhythm ‘Compute Compressed Band Array’ option. The resulting spectral power data were sorted into the bands, delta (0.75-4.00 Hz), theta (4.00-8.00 Hz), alpha (8.00-12.00 Hz), sigma (12.00-16.00 Hz), and beta (16.00-30.00 Hz). We then created one ASCII file per driving segment (ca. 5-6.5 h) with one record per 20-s epoch. Each record held spectral information and human-scorer information. These data were also integrated with driving data recorded from the driver and from the instrumented trucks. The truck and Medilog recorder clocks were compared prior to each trip through a communication cable with software written by the contractor. The fields are shown in Appendix 1. A plot of the first hour of edited relative theta activity from the outbound leg of trip 50 is shown as an example of data recovery (Fig. 2). Segments marked by a polysomnographer as unusable due to motion artifact were deleted from the file. At the time of preparing this manuscript, the first draft of the final report of this investigation was expected to be delivered to the Government at the end of April 1995.
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2.4. Behavioral measures
Each truck was extensively instrumented for lateral lane position, steering wheel position, speed, video image of the roadway and video image of the face. Each driver recorded body temperatures several times per day, provided Stanford Sleepiness Scale (Hoddes et al., 1973) readings several times each day, and was connected to the Vagal Tone Monitor (Porges, 1985) while driving. In addition, driving segments were prefaced and followed by the performance of the Critical Tracking Task (Jex et al., 1966), the Psychomotor Vigilance Task (Dinges and Kribbs, 1991), and the Code Substitution Task (Kennedy et al., 1990). All of these data are in the Paradox database, integrated and time-registered to 1 s accuracy with the EEG data. 3. Summary The majority of data in the Paradox database came from 400 trips contributed by 80 commercial drivers driving both day and night revenue-cargo runs. The EEG data are available in raw digitized files (128 samples/set), and as amplitude spectra for 20s epochs with associated manual-artifact detections by polysomnographers. Integrated-sleep EEG, subjective, driving performance and discrete-task data are also available in the database. The database should serve as an international resource from which many investigators may draw data. To initiate the acquisition of data from the database, the investigator should contact the Office of Motor Carriers in the Federal Highway Administration, U.S. Department of Transportation. Acknowledgements
The EEG portion of this investigation was underwritten with funds from the Federal Highway Administration (FHWA; contract DTFH61-90-C-00053) and from the Trucking Research Institute (TRI) of the American Trucking Associations (ATA). The contractor was Essex Corporation, McLean VA (C.D. Wylie). The subcontractors responsible for EEG data acquisition, reduction and interpretation were the Sleep Disorders Center of the Scripps Clinic and Research Foundation, La Jolla CA (SCRF; M.M. Mitler) and Miller Ergonomics, Lakeside CA. The opinions expressed are those of the author alone and do not necessarily reflect the policies or positions of the FHWA, the ATA, the TRI, Essex Corporation, or the SCRF. Appendix 1
The sleep fields in a database record for one night of sleep are set out below. Fields l-22 contain sleep-EEG data. Fields 23-37 contain oximetry data. 1. Subject 2. Time in bed (TIB, minutes) 3. Lights-off time (seconds, midnightbased) 4. Lights-on time (seconds, midnight-based) 5. Epoch length (30 s) 6. Sleep start-time (seconds, midnight-based) 7. Sleep end-time (seconds, midnight-based) 8. Total sleep time (TST, minutes) 9. Actual sleep time (AST, minutes) 10. AST/TST 11. TST/TIB 12. Wake after sleep onset (WASO, minutes) 13. Movement time
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(minutes, per Rechtschaffen and Kales, 1968) 14. Stage 1 (minutes, per Rechtschaffen and Kales, 1968) 15. Stage 2 (minutes, per Rechtschaffen and Kales, 1968) 16. Stage 3 (minutes, per Rechtschaffen and Kales, 1968) 17. Stage 4 (minutes, per Rechtschaffen and Kales, 1968) 18. Stage REM (minutes, per Rechtschaffen and Kales, 1968) 19. Non-REM (minutes, sum of stages 1-4) 20. Slow-wave sleep (SWS, minutes, sum of stages 3 and 4) 21. Sleep latency (minutes, lights out to first stage 2) 22. REM latency (minutes) 23. Elapsed time (minutes, a system check value) 24. Time excluded (minutes, a system check value) 25. Valid time (minutes, a system check value) 26. Mean % saturation 27. High % saturation 28. Low % saturation 29. % time 1 90% saturation 30.80 I x < 90% saturation (% time) 31. 70 I x < 80% saturation (% time) 32. 60 I x < 70% saturation (% time) 33. Artifact (# events) 34. Desaturation > 3 min (# events) 35. Desaturation < 3 min (# events) 36. Mean low % saturation 37. Mean high % saturation The fields in a database record for driving, where each record represents a 20-s epoch. 1. Elapsed seconds of the segment 2. Total activity (pV2) 3. % delta activity 4. % theta activity 5. % alpha activity 6. % beta activity 7. Manual sleep score (M, W, 1, 2, 3, or 4) from Oxford file References Adrian, ED. (1944). Brain rhythms. Nature, 153, 360-362. Berger, H. (1929). Uber das elektrenkephalogramm des menschen. Arch. Psychiat. Nervenkr., 87, 527-570. Bjerner, B., Helm, A., & Swenson, A. (1955). Diurnal variation in mental performance: A study of threeshift workers. Brir. J. Industrial Medicine, 12, 103-I IO. Brown, I.D. (1994). Drive fatigue. Human Factors, 36(2), 298-314. Browne, R.C. (1949). The day and night performance of teleprinter switchboard
operators.
Occupational
Psychology, 23, l-6. Cameron, C. (1973). Fatigue and driving - A theoretical analysis. Australian Road Research, 5, 36-44. Dinges, D.F., & Kribbs, N.B. (1991). Performing while sleepy: Effects of experimentally-induced sleepiness. In T.H. Monk (Ed.), Sleep, Sleepines and Performance, (pp. 97-128), Chichester, England; John Wiley & Sons. Gawron, V.J., Schiflett, S.G., Miller, J.C., Ball, J.F., & Slater, T. (1988). Effect of Pyridostigmine Bromide on lnjlighr Aircrew Performance. USAFSAM-TR-87-24. Brooks AFB, TX: USAF School of Aerospace Medicine (NTIS AD-A 198-828). Gawron, V., Schiflett, S., & Miller, J. (1987). Pyridostigmine bromide effects on performance and physiology of racfical transport aircrews. Presented at the Annual Meeting, SAFE, Las Vegas, NV. Harris, W.A. (1977). Fatigue, circadian rhythm, and truck accidents. In R.R. Mackie (Ed.), Vigilance: Theory, Operational Performance and Physiological Correlates. New York: Plenum Press. Hoddes, E., Zarcone, V.. Smythe, H., Phillips, R., & Dement, W.C. (1973). Quantification of sleepines: A new approach. Psychophysiology,, 43 l-436. Jasper, H.H. (1960). Unspecific thalamocortical relations. In J. Field, H.W. Magoun & V.E. Hall (Eds.). Handbook of Physiology, Neurophysiology, II, Washington, DC: American Physiological Society. Jasper, H.H. (1958). Report of the committee on methods of clinical examination in electroencephalography. EEG and Clinical Neurophysiology, lo, 370-380. Jex, H.R., McDonnell, J.D., & Phatak, A.V. (1966). A critical tracking task for manual control research. IEEE Transactions on Human Factors in Electronics, HFE-7(4), 138- 145. Kennedy, R.S., Wilkes, R.L., Baltzley. D.R., & Fowlkes, J.E. (1990). Development of microcomputer-
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based mental acuity tests for repeated-measures studies. Final Report, NASA Contract NAS9-17326, Orlando, F.L., Essex Corp. Lewis, N.L., McGovern, J.B., Miller, J.C., & Eddy, D.R. (1987). EEG indices of g-induced loss of consciousness. Proc. of the NATO Aerospace Medical Panel Symposium, Trondheim, Norway. Li, CL., & Jasper, H.H. (1953). Electrode studies of the electrical activity of the cerebrel cortex in the cat. Journal of Physiology, 121, 117-140. Mackie, R.R., & Miller, J.C. (1978). Effects of hours of service, regularity of schedules, and cargo loading on truck and bus driver fatigue. HFR Technical Report 1765-F. Goleta, CA: Human Factors Research, Inc. (NHTSA, DOT-HS-803-799) (NTIS PB-290-957). Mackie, R.R., & Miller, J.C. (1981). Driver fatigue as a function of driving time, regularity of schedules and participation in cargo loading. In D.J. Oborne & J.A. Levis (Eds.), Human Factors in Transport Research. London: Academic Press. Miller, J.C. (September, 1982). A physiological investigation of the relationship between heart rate and monitoring performance. Presented at Review of Air Force Sponsored Basic Research. Biocybernefics/Worklad, Alexandria, VA: AFOSR Directorate of Life Sciences. Miller, J.C. (1988). Physiological measurement of long-haul driver fatigue and alertness. Proceedings FH WA Symposium on Truck and Bus Driver Fatigue (draft). Washington, DC: U.S. Department of Transportation. Miller, J.C., & Mackie, R.R. (1978). Some effects of long-term driving, irregular work schedules, and physical work on driver performance and psychophysiological arousal. Proceedings 19rh Infernational Congress of Applied Psychology, Munich. Miller, J.C., & Mackie, R.R. (1980). Vigilance research and nuclear security: critical review and potenrial applications IOsecurity guard performawe. HFR-TR-2722. Goleta, CA: Human Factors Research, Inc. (National Bureau of Standards contract NBR-GCR-80-201). Miller, J.C., Takamoto, N.Y., Bartel, G.M., & Brown, M.D. (1985). Psychophysiological correlates of long term attention to complex tasks. Behavior Research Methods, Instruments. and Computers, 17(2). 186-190. (USAFSAM-PROC-85-l; NTIS AD-Al62-674) Mitler, M.M. (1989). Two-peak patterns in sleep, mortality and error. Proc. Inr’l, Sympos. on Sleep and Healfh Risk, Springer-Verlag. Morris, T.L., & Miller, J.C. (1983). Electrooculographic indices of changes in simulated flying performance. Presented at The Second Southern Biomedical Engineering Conference, San Antonio, TX. Moruzzi, G., & Magoun. H.W. (1949). Brain stem reticular formation and activation of the EEG. EEG and Clinical Neurophysiology, 455-473. National Transportation Safety Board (1990). Safety Study: Fatigue, Alcohol. Other Drugs, and Medical Factors in Fatal-to-the-Driver Heavy Truck Crashes, Report no. SS-9O/Ol. Washington DC. National Transportation Safety Board. (NTIS PB90-917002). O’Hanlon, J.F., & Kelley, G.R. (1977). Comparison of performance and physiological changes between drivers who perform well and poorly during prolonged vehicular operation. In RR Mackie (Ed), Vigilance: Theory, Operational Performance, and Phvsiological Correlates (pp. 87-I IO), New York: Plenum Press. O’Hanlon, J.F., Miller, J.C., & Royal, J.W. (1975). Effects of simulated surface-eflecr ship motion on crew habitability, Phase II, Vol. 4, Crew cognitive functions. physiological stress, and sleep (HFR- TR-I 757-2). Goleta. CA: Human Factors Research, Inc. (Naval Sea Systems Command, PMS-304-TR-l070-VOL-4; NTIS AD-Aj02-830) Osselton, J.W. (1974). Derivation of electrophyiological signals for the staging of overnight sleep. Pro<,. Eloectrophysiol. Tech&. Assoc., 21, 95- 109. Porges, S.W. (1985). The Vagal Tone Monitor: Application in sleep research. Proc. 9th Ann& Conference of the IEEE Engineering in Medicine and Biology Society. Prokop, O., & Prokop, L. (1955). Ermudung und einschlafen am steuer. Zeitschrifi fur Grrichflichc~ Medizin, 44, 343-355. Rechtschaffen, A., & Kales, A. (Eds.), (1968). A manual of smndurdired terminology, techniques and scoring system for sleep sfuges of human subjecls. Bethesda. MD: U.S. Department of Health, Education. and Welfare, National Institutes of Health.
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Rosekind, M.R., Connell, L.J., Dinges, D.F., Rountree, MS., Graeber, R.C. (1991). Preplanned cockpit rest: EEG sleep and effects on physiological alertness. Sleep Research, 20. Roth, B. (1961). The clinical and theoretical importance of EEG rhythms corresponding to states of lowered vigilance. EEG and Clinical Neurophysiology. 13, 395-399. Schacter, D.L. (1977). EEG theta waves and psychological phenomena: A review and analysis. Biological Psychology, 5, 47-82.
Wilkinson, R.T., & Houghton, D. (1982). Field test of arousal: a portable reaction timer with data storage. Human Factors, 24(4), 487-493. Williams, H.L., Lubin, A., & Goodnow, J.J. (1959). Impaired performance with acute sleep loss. Psychological Monographs, 73(14,484), l-26. Wylie, CD., Mackie, R.R., Mitler, M.M., & Miller, J.C. (1990). (proposal and later amendments). Commercial motor vehicle driverfatigue. reduced alertness and alertness enhancing measures. Research in progress. U.S. Department of Transportation, Federal Highway Administration contracts DTFH61-89-00053 (EEG measurement) and DTFH61-89-C-00096 (performance measurement).
Biological Psychology 40 (1995) 209-222
Batch processing of 10 000 h of truck driver EEG data James C. Miller Miller Ergonomics, 8915 Rocket Ridge Rd. Lakeside. California 92040-4924, USA
Accepted 3 November 1994
Abstract This paper describes the methods used to acquire and reduce a massive amount of EEG data (Wylie et al., 1990). The description is introduced by a review of a previous effort
(Mackie and Miller, 1978). The earlier effort created much of the design philosophy for the second effort. The majority of data in the Paradox database came from 400 trips contributed by 80 commercial drivers driving both day and night revenue-cargo runs of 10 or 13 h each. The sleep and driving EEG data were collected with ambulatory Medilog recorders. Breathing and oxyhemoglobin measures were collected during sleep for sleep-apnea determinations. The sleep and driving-EEG data were placed in raw digitized files (128 samples/second), with the latter also available as compressed-band arrays for 20-s epochs, with associated Rechtschaffen and Kales (1968) manual scoring by polysomnographers for all EEG data. Sleep EEG, subjective driving performance and discrete-task data were also placed in the database, integrated and time-registered to within l-s accuracy with the driving EEG data. Each truck was extensively instrumented for lateral lane position, steering wheel position, speed, video image of the roadway, and video image of the face. Each driver recorded body temperatures several times per day, provided Stanford Sleepiness Scale readings several times each day, and was connected to the Vagal Tone Monitor while driving. In addition, driving segments were prefaced and followed by the performance of the Critical Tracking Task, the Psychomotor Vigilance Task, and the Code Substitution Task. The database should serve as an international resource from which many investigators may draw data. Keywords:
Methods;
* Corresponding userve.com.
EEG; FFT; Trucking; Sleep
author, Tel.: (619) 443-4427; Fax: (619) 443-9347; Internet: 72143.2143@comp-
0301-051l/95/$09.50 0 1995 Elsevier Science Ireland Ltd. All rights reserved SSDI 0301-0511(95)05114-P
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1. Introduction There is recognition of the fact that commercial truck drivers, because of irregularly-scheduled work and night work, suffer on-the-job fatigue. Approximately 30% of fatal-to-the-driver commercial vehicle accidents involve driver fatigue as a primary cause (National Transportation Safety Board, 1990). With fatigue comes the increased likelihood of periods during which we fail to respond to important occurrences in the work environment. These periods may be brief ‘lapses’ (e.g., Wilkinson et al., 1982; Williams et al., 1959) or they may be longer ‘microsleeps’ (e.g., O’Hanlon and Kelley, 1977). There are two portions of the 24-h day during which errors, due probably to lapses and microsleeps, are most prevalent in many occupations (Bjerner et al., 1955; Browne, 1949; Mitler, 1989; Prokop and Prokop, 1955). These periods include the pre-dawn hours and the mid-afternoon hours. During a lapse or a microsleep, we are able to successfully carry on highly-learned, automated behaviours in unremarkable environments. Most of us have experienced an occurrence of a lapse or microsleep while driving. We suddenly realize that we cannot recall any information about the last mile or two driven on the highway. Brown (1994) characterized these occurrences as periods when attention drifts from the task at hand to an internal focus. Objective measures of microsleeps were captured during open-highway driving during an experiment using drowsy drivers in a van equipped with a recording device for brain electrical data and carrying an on-board safety observer with steering and brake controls (O’Hanlon and Kelley, 1977). Drivers’ brain waves indicated a sleep state for up to 15 s while they continued to drive within their lane on a straight segment of freeway. They tended to weave within the lane, but did not crash. Eventually, the drivers may have drifted off the roadway during longer microsleeps had they not been alerted by the safety observer, who assumed control of the vehicle. A number of theories for this ubiquitous phenomenon have been proposed. The theories are based upon data linking human performance to psychophysiological arousal and to behavioral inhibition, reinforcement, filtering, expectancy, and signal detection (Miller and Mackie, 1980). The primary factors which affect the performance of humans required to remain vigilant include the absolute and relative rates at which important and unimportant occurrences occur in the environment; the complexity, timing, and conspicuity of the occurrences; the sense involved (vision, hearing); the time of day; the total work load placed on the individual; the work schedule and its interactions with sleep quality and with bimodal circadian rhythms in human performance; the individual’s motivation; attention from management; and others (Miller and Mackie, 1980). The two investigations described here have focused on psychophysiological aspects of fatigue and sleepiness, and have attempted to control, to the degree possible in field research, several of the factors cited. Several characteristics of the spontaneous EEG are of interest in the investigation of driver fatigue and sleepiness. The disappearance of the alpha rhythm from the awake EEG upon direction of attention to sensory stimuli, voluntary movement, or problem solving was described by Berger (1929) and later termed ‘alpha blocking’ by Adrian (1944). The identification of the midbrain reticular formation as the subcortical structure primarily involved in alpha blocking and behavioral arousal was
J.C. Miller/Biological
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pioneered by Moruzzi and Magoun (1949). Diffuse projections of the mid-line thalamic nuclei were identified as mediating the cortical alpha rhythm (Jasper, 1960), and the alpha rhythm itself appeared to be a result of synchronously occurring inhibitory and excitatory post-synaptic potentials in cortical neurons (Li and Jasper, 1953). Drowsiness may ensue following the appearance of the alpha rhythm if the relaxed subject is not disturbed. In this case the alpha rhythm gives way to some amount of theta activity, additional low-voltage, mixed-frequency EEG, and slow eye movements (Schacter, 1977). Phasic occurrences of sigma waves and large, biphasic Kcomplexes normally signal the onset of sleep, followed by the increasing dominance of delta waves, defining deeper sleep (Rechtschaffen and Kales, 1968; Roth, 1961). This paper describes the methods being used to acquire and reduce a massive amount of EEG data (Wylie et al., 1990). The methods described reflect experience gained in an over-the-road investigation of driver fatigue (Mackie and Miller, 1978) actual flight (Gawron et al., 1987, 1988) and flight simulation (Morris and Miller, 1983), ship simulation (O’Hanlon et al., 1975; Miller, 1976), centrifuge (Lewis et al., 1987) and altitude chamber investigations (Miller and Horvath, 1977a, 1977b), and laboratory investigations (Miller, 1982; Miller et al., 1985). The description is introduced by a review of a previous effort (Mackie and Miller, 1978). This earlier effort created much of the design philosophy for the present effort. 1.1. The 1978 fatigue study.
This project was an investigation of driver fatigue and loss of alertness as possible contributors to accidents experienced by interstate motor carriers of property or passengers (Mackie and Miller, 1978; also see Mackie and Miller, 1981; Miller, 1988; Miller and Mackie, 1978). Four questions were addressed. Is the amount of fatigue experienced by commercial drivers affected (1) by work periods that are irregular with respect to the day-night cycle? (2) by taking their rest periods solely within their vehicle while another person continues driving (sleeper operations)? (3) by performing physical work in addition to driving? (4) by engaging in such operations over an extended number of days? (Mackie and Miller, 1978). In addition to a literature review, a survey, and an accident risk analysis (Harris, 1977), data were collected during three over-the-road investigations using two instrumented commercial vehicles, a 1970 Mack air-conditioned cab-over-engine tractor with sleeper berth plus a 40-ft (12.3 m) semi-trailer with 37 000 lb (16 800 kg) ballast, and a 1958 GMC model PD4104, 35-ft (10.6 m) air-conditioned passenger bus with no ballast. The three data collection conditions were Relay (single) Truck Drivers, Sleeper (paired) Truck Drivers, and Bus Drivers. There were six experienced drivers in each condition. Each Relay driver experienced three different weeks of operations: regular schedule with light physical work, irregular schedule with light physical work, and irregular schedule with moderate physical work. Each Sleeper driver experienced the latter two weeks (their schedules were all irregular). Each Bus driver experienced the first two conditions (light physical work). The EEG was recorded only during driving, i.e., not during sleep. Each Relay
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driver drove about 150.5 h for 3 weeks, each Sleeper driver about 80 h for 2 weeks, and each Bus driver about 90 h for 2 weeks, during operations. The lost data rates for EEG (after editing, described below) were about 12.6%, 13.5%, and 8.6%, respectively. This produced a total product of about 1923 h and a net product of about 1698 h of driving EEG data. The montage was a single channel, Oi-P3 (Jasper, 1958). A custom-made, bipolar preamplifier (lo4 gain, 80 db common-mode rejection, 0.3-s time constant) was placed near the electrodes, and the data were recorded in pulse-width modulation format. Data from the first 18 s of each minute were digitized as 2056 samples (about 114 samples/second), allowing a 0.055 Hz fundamental frequency and bin width for frequency analysis. Epochs with values below 5 PV p-p throughout the 18-s epoch, and peaks within epochs above 100 PV p-p were ignored automatically. The EEG epochs were zero-suppressed and tapered, then subjected to a fast Fourier transform (FFT). Spectral relative power estimates were aggregated into theta (5.005-7.975 Hz), alpha (8.030-12.045 Hz) and beta (16.005-30.030 Hz) bands. Delta-band data were not used due to expected eye movement artifact. Band-specific relative power data were interactively edited by highway segment within each trip. Each variable was displayed in a graphic scatterplot which included a linear regression line for the highway segment. Regression line slopes and mid-points were stored as reduced data representing each highway segment. The regression line mid-points represented the average band-specific energy value for the highway segments of about 15 min to 1 h in length. Since the experimental highway route was an out-and-back arrangement, data from the inbound highway segments, late in each trip, were compared to data from the outbound segments, early in each trip, by analysis of variance. Since changes in alpha activity tend to be ambiguous, we focused on theta and beta activities in the analysis. (By ambiguous, I refer to the alpha-blocking triggered both by alerting and by the onset of Stage 1 drowsiness). Statistically significant and nearly significant differences (p c 0.05 or p c 0.10, respectively) occurred in nearly all trips in at least one of the two frequency bands, relative theta and beta. The effect sizes were generally in the range of 2-10% increases or decreases. The effects were generally interpretable as ‘arousal’ (increased beta and/or decreased theta) or ‘fatigue’ (decreased beta and/or increased theta). Looking back at the data, there were a couple of interesting, specific occurrences. First, during Relay operations, relative theta activity was higher (p < 0.05) during two night driving segments on Interstate Highway 5 (I-5) than at other times on narrower highways (Mackie and Miller, 1978, Fig. 59). Lane tracking was not impaired for these two segments (Fig. 56). Second, during Sleeper operations, relative theta activity was higher (p c 0.05 and p < 0.10) for a OO:OO-05:OO h run across several highway types, compared to other times of day (Fig. 78). Lane tracking was not impaired for that run (Fig. 75). Bus operations were not demanding enough to fatigue the drivers to the point of elevated theta activity. These observations suggested that, as expected, drowsiness occurred often during the pre-dawn hours. They also indicated that lane-tracking performance was not grossly disturbed during that period. This pattern may have reflected mild
213
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J. C. Miller / Biological Psychology 40 (I 995). 209-222
drowsiness and reduced vigilance accompanied by highly automated lane-tracking behavior. In this situation, a subtle system change, like a gradual drift to the edge of the road, may not be noticed. Compressed spectral array plots of sequential data epochs showed waxing and waning of relative theta power during driving (Fig. 1). It appeared from plots such as these that, in a future investigation, a theta:beta ratio might provide a reasonably good physiological indicator of possible drowsiness episodes. Three of the conclusions we drew concerning truck Relay and Sleeper operations included evidence gained from EEG data. Firstly, some cumulative fatigue occurs during 6 consecutive days of relay operations. Evidence included low cortical arousal on the first day and high cortical activation on the sixth day of operations for drivers on the regular schedule. The philosophy here was that greater cortical activation reflected the effort to remain awake (Cameron, 1973). Secondly, participation in moderately heavy cargo-loading to the extent engaged in by many relay truck drivers increases the severity of fatigue associated with irregular working schedules. Evidence included less theta and more beta in the EEG in the moderate work condition suggesting that, despite any possible benefits of physical work, greater cortical activation was required to maintain performance. Finally, Sleeper-driver fatigue, physiological state, and performance are strongly affected by time of day. Evidence included significant elevations in cortical activation (increased beta, decreased theta) during late night or early morning trips, indicating increased effort to remain alert. These conclusions focussed strongly on mean values of relative EEG band activity from highway segments ranging from 15 min to 1 h long. They did not take into account the occurrence and potential effects of brief microsleeps, nor did we detect and calculate the frequency of microsleeps. There were two other, more obvious limitations to our EEG acquisition and analysis process. Firstly, we were technically limited by the computer network and the time required to digitize the raw data. Because of the limitations, we chose to look at only 18 s of EEG data from each minute. We rationalized that EEG manifestations of arousal probably wax and wane across periods of several minutes, and that we would not miss these variations if we sampled regularly from each minute. However, we learned that an apparent microsleep can be completed during about 15 s (O’Hanlon and Kelley, 1977). Secondly, we did not record the sleep of the drivers. We had the expertise and equipment available to implement full Rechtschaffen and Kales (1968) assessments. However, limits in contract scope precluded taking that investigative path. In the present investigation, described below, we avoided these two limitations. Even though the investigation had these limitations, it provided a strong impetus for us to acquire EEG data during subsequent investigations of operator fatigue. In addition, during the 198Os,the concept of the sleep disorders clinic was realized in most major medical facilities. This occurrence brought formal organization and accreditation to clinical polysomnographic methods and personnel. This clinical use of the EEG helped establish the EEG as a widely accepted indicator of the state of human consciousness and the quality of sleep acquired during rest periods.
J. C. Miller
Table I Experimental
/ Biological
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40 ( 1995).
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215
design
Schedule
Drive time per duty period
Day driving Night driving
10 h
13 h
n = 20 n = 20
n = 20 n = 20
1.2. The present fatigue study.
The present investigation was designed (Wylie et al., 1990) to answer the following questions (paraphrased): (1) do commercial vehicle drivers experience operationally significant losses of alertness due to fatigue within current daily and weekly hours of service drive-time limits? (2) if so, after how many hours of driving do these debilitating effects occur? (3) does loss of alertness due to fatigue occur earlier, or is it more severe, on irregular vs. regular schedules? (4) do older drivers (> 45 years) experience an earlier or more severe loss of alertness than younger drivers? (5) does quality or quantity of sleep influence the latency or severity of loss of alertness? (6) is there cumulative fatigue across days? (7) what practical countermeasures might be used to combat loss of alertness due to fatigue? 2. Methods 2.1. Experimental design and procedures Twenty male, commercial drivers participated in cells of a 2 x 2 matrix of experimental conditions, as shown in Table 1. The 10-h driving per duty period occurred on runs between St. Louis and Kansas City. The 13-h driving per duty period occurred on runs between Toronto and Montreal. All trips carried revenue-producing Table 2 Planned hours of data acquisition
by experimental
Condition
Trips
Hours
10-h 13-h Recovery Regional
200 200 55 100
8 8 8 8
Total
condition
sleep
Hours IO I3 I3 5
driving
Total hours 3600 4200 II55 I300 10 255
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J. C. Miller / Biological Psychology 40 ( 1995). 209-222
cargo for the carriers who assisted us (Yellow Freight, Consolidated Freightways, and in Canada, SLH, a Sears subsidiary). Ten drivers in each cell fell in the 21-45 year age range and the other 10 were over 45 years. Each driver operated for 1 week in this design, producing five trips, for a total of 400 trips. Each driver provided informed consent to participate as a subject in the investigation. There was also a Transport Canada-sponsored add-on investigation of 55 trips, designed to allow an examination of recovery functions following daytime runs between Toronto and Montreal. A regional operations cell with about 100 trips was also planned, but not carried out. A 3-digit trip number described the cycle, sleep-drive-drive, where sleep was the major sleep period, the first drive was the outbound leg (5-6.5 h), and the second drive was the inbound leg. The fifth trip in the 13-h conditions contained only a sleep period and no driving periods. The EEG was recorded during sleep (nominally 8 h) and driving (nominally 10 or 13 h). We planned 10 255 h of EEG acquisition, as shown in Table 2. In fact, the drivers slept much less than 8 h per shift. In addition, the 13-h condition allowed only four drives instead of five drives per week (however, five sleep periods were measured). Thus, we concluded data collection with about 7500 h of EEG data. 2.2. Signal acquisition The recording montages are shown in Table 3. The electrode placements (channels l-5) allowed signal acquisition that supported the standardized polysomnographgic scoring method (Rechtschaffen and Kales, 1968). During sleep, channels 5-7, in conjunction with fingertip oximetry, allowed assessments of sleep apnea. The Oxford Medilog 9000 tape system (Oxford Instruments Ltd, Abingdon, Oxon, England) was chosen because of its track record for acquiring data reliably. Table 3 Electrode and sensor placements for sleep and driving Channel 1 2 3 4 5 6 7 8 Indifferent
Sleep
Driving
C4-AI
C4-A
01742
01 -A2
LOC-ROC
LOC-A, ROC-A,
C3-A2
EMG-EMG (chin) Chest motion Oral/nasal airflow ECG cz (two)
I
C3-A2
Cz (two)
The central (C), auricular (A) and occiptial(0) electrode sites were consistent with the International IO-20 System (Jasper, (1958). The left-outer-canthus-right-outer-canthus (LOC-ROC) derivation was described, for example, by O&ton (1974).
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Data acquisition was the most critical aspect of our data-handling task. The Medilog has a long history of reliability and had performed well recently for the NASA-Ames Research Center Human Factors Division during its cockpit-napping studies for the FAA (Rosekind et al., 1991). Concomitantly, we used the Oxford 9200 datareduction system to decode the tapes and digitize the EEG data. 2.3. Data reduction For administrative purposes only, the EEG tapes were auto-scored at 20 x real time on the 9200 (Oxford software version 8.7). The sleep-stage results of the autoscoring process were ignored. However, the auto-score process set up the segment start and stop times and then allowed the segment to be digitized at 60 x real time and stored on the computer’s hard disk. Subsequently, the digitized file was transferred to a removable optical disk in the 9200. Files were sent to the Sleep Disorders Division of the Scripps Clinic and Research Foundation (SCRF) from the field on approximately forty-five 960-megabyte, write once, read many (WORM) optical disks, providing some protection against overwriting valuable data. At SCRF, polysomnographers copied the data from WORM disks back to a second 9200’s hard disk. They manually scored sleep- and drivingsegment EEGs using the graphics and data-logging capabilities of the 9200 and Rechtschaffen and Kales (1968) standards. For the major sleep period and for naps, the epoch length was 30 s. We combined the manual scoring output of Oxford 9200 software with the oximetry output of Profox oximetry software (Profox Associates, Inc., version 12s). Thus, each 30-s epoch was human-coded for sleep stage and oximetry-coded, as well. The two files were synchronized to 1 min accuracy by the field technicians’ matching of the oximeter time to the Medilog recorder time. If a driver was found to have clinically significant sleep apnea, SCRF specialists in apnea scored all of that driver’s sleep records. The allowable EEG scores were (Rechtschaffen and Kales, 1968): M, motion artifact (unusable signal); W, awake (usable signal); 1, stage 1; 2, stage 2; 3, stage 3; 4, stage 4; R, stage REM. The resulting reduced data were structured as one record per night of sleep. One Paradox (Borland International, Inc., Scotts Valley, CA) database file was created to hold the many nights of sleep data. The fields of each record are listed in Appendix I. These data were integrated into the database with driving data recorded from the driver and from the instrumented trucks. For driving periods, the epoch length was 20 s. We focused our manual scoring on the Cs-A2 channel or on the &-A, channel, using the cleaner one. We combined the frequency-analysis output of Rhythm (Stellate Systems, Westmount, Quebec, Canada, versions 7.1 and 8.0) frequency-analysis software with the manual scoring output of Oxford 9200 software. Thus, each 20-s epoch was human-coded as shown above. The two files were synchronized by virtue of the former serving as the source for the latter. To use the Rhythm frequency-analysis software, we converted a digitized EEG data file from the Oxford system using an Oxford-provided conversion program
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Power, No “M”
, i
7000 6000 (y 5000
(
0
20
40
60
80
100 120 140
Minutes
Fig. 2. Spectral estimates of theta band activity as a function of drive time.
(crhythm.exe version 2.1 from Oxford). The conversion from Oxford to Stellate file format took about 3 min for a 20-megabyte EEG file. The Stellate file format held digitized data, sampled at 128 sample&c, in 4-s (512-sample) epochs. These were concatenated into 20-s epochs for processing only (the original data remained in digitized 4-s epochs). Each 20-s epoch was zero-suppressed, tapered with a 40% cosine function, then subjected to a fast Fourier transform (FFT) with the Rhythm ‘Compute Compressed Band Array’ option. The resulting spectral power data were sorted into the bands, delta (0.75-4.00 Hz), theta (4.00-8.00 Hz), alpha (8.00-12.00 Hz), sigma (12.00-16.00 Hz), and beta (16.00-30.00 Hz). We then created one ASCII file per driving segment (ca. 5-6.5 h) with one record per 20-s epoch. Each record held spectral information and human-scorer information. These data were also integrated with driving data recorded from the driver and from the instrumented trucks. The truck and Medilog recorder clocks were compared prior to each trip through a communication cable with software written by the contractor. The fields are shown in Appendix 1. A plot of the first hour of edited relative theta activity from the outbound leg of trip 50 is shown as an example of data recovery (Fig. 2). Segments marked by a polysomnographer as unusable due to motion artifact were deleted from the file. At the time of preparing this manuscript, the first draft of the final report of this investigation was expected to be delivered to the Government at the end of April 1995.
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2.4. Behavioral measures
Each truck was extensively instrumented for lateral lane position, steering wheel position, speed, video image of the roadway and video image of the face. Each driver recorded body temperatures several times per day, provided Stanford Sleepiness Scale (Hoddes et al., 1973) readings several times each day, and was connected to the Vagal Tone Monitor (Porges, 1985) while driving. In addition, driving segments were prefaced and followed by the performance of the Critical Tracking Task (Jex et al., 1966), the Psychomotor Vigilance Task (Dinges and Kribbs, 1991), and the Code Substitution Task (Kennedy et al., 1990). All of these data are in the Paradox database, integrated and time-registered to 1 s accuracy with the EEG data. 3. Summary The majority of data in the Paradox database came from 400 trips contributed by 80 commercial drivers driving both day and night revenue-cargo runs. The EEG data are available in raw digitized files (128 samples/set), and as amplitude spectra for 20s epochs with associated manual-artifact detections by polysomnographers. Integrated-sleep EEG, subjective, driving performance and discrete-task data are also available in the database. The database should serve as an international resource from which many investigators may draw data. To initiate the acquisition of data from the database, the investigator should contact the Office of Motor Carriers in the Federal Highway Administration, U.S. Department of Transportation. Acknowledgements
The EEG portion of this investigation was underwritten with funds from the Federal Highway Administration (FHWA; contract DTFH61-90-C-00053) and from the Trucking Research Institute (TRI) of the American Trucking Associations (ATA). The contractor was Essex Corporation, McLean VA (C.D. Wylie). The subcontractors responsible for EEG data acquisition, reduction and interpretation were the Sleep Disorders Center of the Scripps Clinic and Research Foundation, La Jolla CA (SCRF; M.M. Mitler) and Miller Ergonomics, Lakeside CA. The opinions expressed are those of the author alone and do not necessarily reflect the policies or positions of the FHWA, the ATA, the TRI, Essex Corporation, or the SCRF. Appendix 1
The sleep fields in a database record for one night of sleep are set out below. Fields l-22 contain sleep-EEG data. Fields 23-37 contain oximetry data. 1. Subject 2. Time in bed (TIB, minutes) 3. Lights-off time (seconds, midnightbased) 4. Lights-on time (seconds, midnight-based) 5. Epoch length (30 s) 6. Sleep start-time (seconds, midnight-based) 7. Sleep end-time (seconds, midnight-based) 8. Total sleep time (TST, minutes) 9. Actual sleep time (AST, minutes) 10. AST/TST 11. TST/TIB 12. Wake after sleep onset (WASO, minutes) 13. Movement time
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(minutes, per Rechtschaffen and Kales, 1968) 14. Stage 1 (minutes, per Rechtschaffen and Kales, 1968) 15. Stage 2 (minutes, per Rechtschaffen and Kales, 1968) 16. Stage 3 (minutes, per Rechtschaffen and Kales, 1968) 17. Stage 4 (minutes, per Rechtschaffen and Kales, 1968) 18. Stage REM (minutes, per Rechtschaffen and Kales, 1968) 19. Non-REM (minutes, sum of stages 1-4) 20. Slow-wave sleep (SWS, minutes, sum of stages 3 and 4) 21. Sleep latency (minutes, lights out to first stage 2) 22. REM latency (minutes) 23. Elapsed time (minutes, a system check value) 24. Time excluded (minutes, a system check value) 25. Valid time (minutes, a system check value) 26. Mean % saturation 27. High % saturation 28. Low % saturation 29. % time 1 90% saturation 30.80 I x < 90% saturation (% time) 31. 70 I x < 80% saturation (% time) 32. 60 I x < 70% saturation (% time) 33. Artifact (# events) 34. Desaturation > 3 min (# events) 35. Desaturation < 3 min (# events) 36. Mean low % saturation 37. Mean high % saturation The fields in a database record for driving, where each record represents a 20-s epoch. 1. Elapsed seconds of the segment 2. Total activity (pV2) 3. % delta activity 4. % theta activity 5. % alpha activity 6. % beta activity 7. Manual sleep score (M, W, 1, 2, 3, or 4) from Oxford file References Adrian, ED. (1944). Brain rhythms. Nature, 153, 360-362. Berger, H. (1929). Uber das elektrenkephalogramm des menschen. Arch. Psychiat. Nervenkr., 87, 527-570. Bjerner, B., Helm, A., & Swenson, A. (1955). Diurnal variation in mental performance: A study of threeshift workers. Brir. J. Industrial Medicine, 12, 103-I IO. Brown, I.D. (1994). Drive fatigue. Human Factors, 36(2), 298-314. Browne, R.C. (1949). The day and night performance of teleprinter switchboard
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Wilkinson, R.T., & Houghton, D. (1982). Field test of arousal: a portable reaction timer with data storage. Human Factors, 24(4), 487-493. Williams, H.L., Lubin, A., & Goodnow, J.J. (1959). Impaired performance with acute sleep loss. Psychological Monographs, 73(14,484), l-26. Wylie, CD., Mackie, R.R., Mitler, M.M., & Miller, J.C. (1990). (proposal and later amendments). Commercial motor vehicle driverfatigue. reduced alertness and alertness enhancing measures. Research in progress. U.S. Department of Transportation, Federal Highway Administration contracts DTFH61-89-00053 (EEG measurement) and DTFH61-89-C-00096 (performance measurement).