eeg quantification of alertness: methods for early identification of individuals most susceptible to...

Proceedings of the SPIE Defense and Security Symposium, Biomonitoring for Physiological and Cognitive Performance during Military Operations. John A. Caldwell, Nancy Jo Wesensten, Eds. Vol. 5797: pgs. 78-89. 2005.

EEG quantification of alertness: Methods for early identification of individuals most susceptible to sleep deprivation

Chris Berka1*, Daniel J. Levendowski1, Philip Westbrook1, Gene Davis1, Michelle N. Lumicao1,

Richard E. Olmstead2, Miodrag Popovic3, Vladimir T. Zivkovic1, Caitlin K. Ramsey1

1Advanced Brain Monitoring, Inc., 2850 Pio Pico Drive, Suite A, Carlsbad, CA USA 92008 2VA Greater Los Angeles Healthcare System, 11301 Wilshire Blvd., Los Angeles, CA USA 90073

3University of Belgrade, Faculty of Electrical Engineering, Serbia and Montenegro

ABSTRACT

Electroencephalographic (EEG) and neurocognitive measures were simultaneously acquired to quantify alertness from 24 participants during 44-hours of sleep deprivation. Performance on a three-choice vigilance task (3C-VT), paired-associate learning/memory task (PAL) and modified Maintenance of Wakefulness Test (MWT), and sleep technician-observed drowsiness (eye-closures, head-nods, EEG slowing) were quantified. The B-Alert® system automatically classifies each second of EEG on an alertness/drowsiness continuum. B-Alert classifications were significantly correlated with technician-observations, visually scored EEG and performance measures. B-Alert classifications during 3C-VT, and technician observations and performance during the 3C-VT and PAL evidenced progressively increasing drowsiness as a result of sleep deprivation with a stabilizing effect observed at the batteries occurring between 0600 and 1100 suggesting a possible circadian effect similar to those reported in previous sleep deprivation studies. Participants were given an opportunity to take a 40-minute nap approximately 24-hours into the sleep deprivation portion of the study (i.e., 7 PM on Saturday). The nap was followed by a transient period of increased alertness. Approximately 8 hours after the nap, behavioral and physiological measures of drowsiness returned to levels prior to the nap. Cluster analysis was used to stratify individuals into three groups based on their level of impairment as a result of sleep deprivation. The combination of B-Alert and neuro-behavioral measures may identify individuals whose performance is most susceptible to sleep deprivation. These objective measures could be applied in an operational setting to provide a “biobehavioral assay” to determine vulnerability to sleep deprivation. Keywords: Sleep deprivation, individual differences, EEG, alertness, drowsiness, fatigue, vigilance performance, real-time monitoring, sleep debt.

1. INTRODUCTION Successful military operations require rapid and accurate decision-making and sustained vigilance often in challenging environments. Vigilance, short-term memory and decision-making are severely impacted by sleep deprivation with potentially dangerous consequences. The management of fatigue is increasingly considered a serious public health and safety concern1-6, since impaired vigilance is now believed to be a primary contributor to transportation and industrial accidents7-12. Recent NASA technical reports reveal that pilots often evidence brief episodes of unintentional sleep while flying13-16. The technical complexity and 24-hour schedule of the contemporary workplace demands the ability to sustain high levels of performance for extended periods of time17. As automation replaces manual labor, maintaining vigilance becomes more difficult with performance decrements increasing with time-on-task18,19. The adverse effects of sleep loss are calculated by quantifying the cumulative hours of sleep debt and accounting for interactions with circadian cycles20,21. The effects of even small amounts of sleep loss each night accumulate over time resulting in a “sleep debt”; as sleep debt increases, alertness, memory and decision-making are increasingly impaired20-

24. Individuals have been shown to become accustomed to this chronic accumulation of fatigue and are often unaware of the impact on their performance. Recent studies have suggested individuals may differ in their vulnerability to sleep deprivation25-32. These studies suggest that a fatigue management model that takes into account individual differences in susceptibility to sleep loss may be required to provide a safe, efficient, and highly productive workplace. * [email protected]; phone 1 760 720 0099; fax 1 760 720 0094; b-alert.com

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The electroencephalogram (EEG) is widely regarded as the physiological “gold standard” for the assessment of alertness, even though numerous physiological parameters, including cardiovascular indices, pupil diameter and eye closures have been employed. Subtle shifts from vigilance to drowsiness can be identified by quantifying changes in EEG waveforms33-36. The measurement of EEG indices of alertness-drowsiness in the laboratory setting has resulted in highly sensitive and reliable correlations with performance, including predictions on a second-by-second basis37-39. Torsvall and Akerstedt40 identified EEG patterns highly predictive of sleep onset. In their research, eye closures or slow eye movements occurred too late in the behavioral chain of events to be useful in providing an early warning drowsiness detection system. Similarly, in a study of sleep-deprived professional drivers using a driving simulator, EEG was a reliable indicator of alertness levels and showed evidence of fatigue prior to deteriorations in driving performance41. Recent investigations have proven that high quality EEG can be recorded in difficult environments including: airplane cockpits, truck cabins and train quarters42-45. This paper presents the results from a 44-hour sleep deprivation study designed to extend the validation of the B-Alert EEG indices to characterize changes along the alertness-drowsiness continuum31,46-48. A number of measures commonly used to measure sleep-deprived performance were employed to validate the EEG indices, including performance measures derived from vigilance tasks, memory tasks, a maintenance of wakefulness task, and visual scoring of eye closures and EEG. Data are also presented that suggest variability in performance and B-Alert indices may be related to identifiable individual differences in susceptibility to sleep deprivation.

2. METHODOLOGY 2.1 Protocol Twenty-seven healthy participants consented to participate in a sleep-deprivation study. All participants were screened for sleep disorders, head trauma, attention deficit disorders, and sleep patterns that did not allow at least 6.5 hours of sleep per night. Sleep logs were used to monitor sleep patterns for the four nights preceding the study. Each participant started their study by performing a 4-hour baseline screening session at the Advanced Brain Monitoring laboratory approximately two hours after awakening (i.e., start time was between 8 and 10 AM) on a Friday morning. Participants reported to Pacific Sleep Medicine Services (PSMS), La Jolla at approximately at 7 PM that same evening for the sleep deprivation portion of the study. Between 7 PM on Friday and 5 AM on Sunday, participants completed a total of nine, three-hour test batteries at PSMS with a one-hour break between each battery. Each battery consisted of two 20-minute vigilance tasks, four memory tasks, a 40-minute Maintenance of Wakefulness Task, and a 10-minute PVT-19249 (Ambulatory Monitoring, Inc., Ardsley, New York). Subjective sleepiness scales were administered periodically throughout each test battery and participants were video taped during the test batteries. Participants were observed during the one-hour breaks to ensure that they did not fall asleep. Approximately 24-hours into the sleep deprivation portion of the study (i.e., 7 PM on Saturday), participants were provided the opportunity to take a 40-minute nap. Participants were allowed 20-minutes to fully awaken from the nap prior to continuing with battery #7. All participants were provided transportation to and from their sleep deprivation study. 2.2 Physiological data acquisition Ag/AgCl electrodes were applied at Fz, Cz, POz, and Oz with left and right earlobes as the reference and ground. Differential recordings for FzPOz and CzPOz were used to compute the B-Alert EEG indices. Vertical and horizontal EOG were recorded referentially. EEG and EOG were acquired with Teledyne amplifiers, low pass filter at 75 Hz and high pass filter at 0.5 Hz, with fixed gains at 10,000 and 2,000, respectively. The dynamic range of the EEG channels was +/- 250 µV. The sampling rate was 256 s/s for all channels. The acquisition software integrated outputs from the task software (described below) into the EEG recording for off-line synchronization of the EEG with the presentation, response, and type of response (correct, incorrect or no-response) of each task stimuli. 2.3 Task description Three software programs, developed by the investigators, were used in this study. The tasks included a 3-Choice Vigilance Task (3C-VT), a Paired Associate Learning/Memory Task (PAL), and a modified Maintenance of Wakefulness Task (MWT).



2.3.1 3-Choice Vigilance Task (3C-VT) The 3C-VT (Figure 1.a.) incorporates features of the most common measures of sustained attention, such as the Continuous Performance Test50-52, the Wilkinson Reaction Time Test53,54and the PVT-19249,55, and was designed to allow synchronization of the task and EEG data for subsequent off-line analysis. The 3C-VT is easy to perform and relatively insensitive to practice or training effects31,47,56. Concurrent validity was established in previous sleep deprivation studies by correlation with behavioral47,56,57. The task presents three classes of geometric shapes; the primary shape is presented 70% of the session and two distracter shapes are randomly interspersed throughout the balance of the session. Each shape is presented for 200 milliseconds with inter-stimulus intervals ranging from 1.5 to 10 seconds over the 20-minute session. Each shape is approximately 6 cm as presented on a 17” computer monitor. Performance measurements include reaction time of correct and incorrect responses. A 1.5 second threshold was used to measure errors of omissions. A brief training period is provided prior to the start of the testing period to minimize practice effects.

Training: Memorize 20 images

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Duration: 20 minutesDuration: 20 minutesFig. 1.a. Fig. 1.b.

Figure 1: a. 3-Choice Vigilance Task. b. Paired Associate Learning Task 2.3.2 Paired Associate Learning/ Memory Task (PAL) The PAL (Figure 1.b.) evaluates attention, distractibility and encoding and image recognition memory. During the training session, a group of 20 images are presented sequentially twice. The testing session randomly presents the 20 training images interspersed with 80 additional images. Participants are instructed to identify the images in the training set. Each image is presented for 500 milliseconds during the encoding (training) period and 200 milliseconds during the testing period. Testing images were presented every 2 seconds. Five equivalent image categories were used in the study including animals, food, household goods, sports and travel. A practice session was provided at the start of the baseline screening session to ensure participants understood the task. The training images and image categories, and sequence of presentation of the training and testing images within each session were consistent across participants and test batteries. In addition to classifying correct and incorrect responses and measuring the corresponding reaction times, omissions were classified if the participant had not responded by the time the next image was presented (e.g., after 2 seconds). 2.3.3 Modified Maintenance of Wakefulness Task (modified MWT) The modified MWT, similar to the Osler Test58, presents a 10 cm circular image for 200 milliseconds in the center of the computer monitor, repeating every two seconds. Participants are instructed to press the space bar each time they see the image. The session automatically terminates when the participant does not response for 90 consecutive seconds or after 40 minutes have been completed. Intra-class correlations between the 3C-VT and PAL performance results from two fully-rested morning sessions for 64 participants demonstrate the reliability of these tasks46,59 (Table 1). Task Measure Day 1 X + S.D. Day 2 X + S.D. Intra Class Correlation, p < 0.0013C-VT % Correct 96.9 + 2.2 96.4 + 2.4 0.844 3C-VT Reaction Time 0.61 + .06 0.64 + .07 0.830 PAL % Correct 96.4 + 3.2 96.3 + 4.6 0.806

Table 1: Intra-class correlations between two AM sessions for 64 participants during the 3C-VT and PAL.



3. DATA REDUCTION AND STATISTICAL ANALYSES

3.1 Participants Of the 27 participants that enrolled in the study, two experienced flu symptoms and terminated their study after battery #3 and the performance data was lost for one participant. The data from 24 participants (males: n = 18, females: n = 6, age 21 – 38) was available for analysis. High quality video recordings from 18 of the 24 participants were available for visual scoring of the video and EEG. 3.2 Technician scoring Methods used for visual technician scoring of the EEG and video recording were developed by Dr. Merrill Mitler in a previous round-the-clock study monitoring 80 truck drivers in the US and Canada44. The synchronized time-stamp on the video and EEG recordings allowed concurrent visual scoring of the 3C-VT and PAL sessions. One technician scored all of the sessions for a participant, beginning with battery #9 (when the participant was most tired) and progressing to battery #10. Finger-tapping patterns in the modified MWT sessions were used as a reference to assist in recognizing micro-sleeps in the EEG data. Using a combination of video observations and EEG scoring in 20-second epochs, according to a modified Rechtschaffen & Kales protocol, technicians scored each 1-second epoch of the 3C-VT and PAL sessions as fully awake, moderately drowsy, extremely drowsy, or asleep. The percentage of one-second epochs classified into each of the technician scoring groups was tallied during each 3C-VT and PAL tasks. For data analysis purposes, the epochs scored as extremely drowsy and asleep are also combined into a single category called drowsy. 3.3 B-Alert EEG indices 3.3.1 Artifact detection and decontamination or rejection At the start of the signal processing a 60 Hz notch filter is applied to the EEG. A number of artifacts in the time-domain EEG signal, including spikes, amplifier saturation and excursions that occur during the onset or recovery of saturations, are automatically detected and decontaminated in each channel separately. Changes in amplitude for at least 5 consecutive data points in the amplifier saturation range as determined by the amplifier characteristics are identified as saturations. Sudden and constant changing EEG amplitudes across 3, 4 or 5 data points with only one zero crossing in a 128 data point region are classified as excursions. Bi-directional amplitude changes > 40 µV across 3, 5, or 7-data point are classified as spikes. The start and end of the saturation, excursion and spike ranges are determined and used in subsequent decontamination. The EEG signal is then decomposed by 6 level Stationary (nondecimated) wavelet transform60 into wavelet bins from 0-2, 2-4, 4-8, 8-16, 16-32, 32-64 and 64-128 Hz. Identification of eye blinks is determined using a discriminant function analysis (DFA) that classifies each data point as an eye blink, theta wave, or no eye blink or theta wave. The variables used in the discriminant function were selected by stepwise regression analysis and include wavelet bins between 0-2 and 16-32 Hz for the data point being tested and predictive wavelet bins from up to 80 data points before and after the data point being tested from the differential recordings from FzPOz and CzPOz. Data points contaminated with previously identified artifacts are replaced with the mean wavelet bin results from the previous 10 seconds of artifact free data points. After data points contaminated with eye blinks are identified, the start and end of the eye blink region is detected. Wavelet coefficients in the eye blink range in the 0-2, 2-4 and 4-8 Hz bins are replaced with artifact free mean wavelet values in each channel of EEG. Once the eye blink is decontaminated, the EEG is recomposed using the wavelet bins from 2 – 64 Hz. Zero values are then substituted for data points in the saturation, excursion or spike ranges extending to the second zero crossing before or after each artifact. Decontaminated EEG is then segmented into overlapping 256 data-point windows called overlays. An epoch consists of three consecutive overlays overlapping by 128 data-points. Fast-Fourier transform is applied to each overlay of the decontaminated EEG multiplied by the Kaiser window (α = 6.0) to compute the power spectral densities (PSD). The PSD values are adjusted to take into account zero values inserted for artifact contaminated data points. The power in the wavelet bin from 64 – 128 Hz is used to identify epochs contaminated with excessive EMG. Overlays with excessive muscle activity (EMG) or with fewer than 128 valid data points are rejected. The remaining overlays are then averaged to derive PSD for each epoch. Epochs with two or more overlays with EMG or missing data are classified as invalid. For each channel, the logged PSD values are derived for each one-Hz bin from



3 Hz to 40 Hz. These 38 variables represent absolute PSD variables. Relative PSD variables are obtained by subtracting absolute variables from the logged total PSD in the 3 to 40 Hz band. 3.3.2 Classification of B-Alert EEG indices The B-Alert EEG indices are derived from a four-class quadratic DFA that classifies each valid one-second epoch as high vigilance, low vigilance, relaxed wakefulness, or sleep-onset. The absolute and relative PSD variables selected from FzPOz and CzPOz for the B-Alert model were derived empirically using a database of healthy participants under fully-rested and sleep-deprived conditions. The DFA coefficients for the four-class model are fitted to the individual’s unique EEG patterns using baseline data obtained from three 5-minute baseline conditions: finger-tapping eyes open (EO) and eyes closed (EC), and 5 minutes of the 3C-VT. For purposes of data analysis the relaxed wakefulness and sleep-onset classifications are combined into a single category called drowsy. 3.4 Data reduction Summary data were computed for the reported sessions across the 10 batteries. The percentage of correct (%correct) and missed responses (%missed), and mean reaction times (RT) were computed for the 3C-VT and PAL performance measures. The percentage of epochs classified as awake, moderately drowsy, extremely drowsy, asleep and drowsy were computed for the technician scoring. The percentage of epochs classified as high vigilance, low vigilance, relaxed wakefulness, sleep-onset, and drowsy were computed using the B-Alert Indices. The amount of time that participants were able to stay awake during the modified MWT were derived. 3.5 Statistical analyses To validate the capability of the B-Alert indices to measure fatigue attributed to sleep deprivation, task performance and technician scoring were compared using the B-Alert sleep-onset and drowsy classifications. Canonical correlation analysis was performed using CANCORR macro in SPSS (Release 8.0). For each individual, canonical correlations across the 10 time-points were calculated for the following pairs of variable sets: 1) EEG variables from B-Alert (%Sleep-Onset and %Drowsy) and task performance variables (%correct, %missed, RT), 2) EEG variables (%Sleep-Onset and %Drowsy) and measures of technician scoring (% classified by technician as fully awake, moderately drowsy, extremely drowsy, asleep). To improve the normality of the distribution of the variables, logarithmic transformations were applied to the technician observation variables. Canonical correlation analysis was utilized to simplify the analysis as it provides a single aggregate measure of association compared to examining the set of pairwise bivariate correlations; it is especially appropriate when a moderate to high degree of intra-correlation within the two sets of variables is expected. Canonical variates are linear functions of the variables. Canonical roots are determined in order of magnitude such that the maximum amount of variance in common between canonical variates for the two sets of variables is accounted for in each step. In other words, the canonical variates at each step are linear functions that maximize the relationship between the two sets of variables. There are as many canonical roots (hence canonical correlations) as there are variables in the smaller of the two sets; however, most interest is on the first canonical root in terms of magnitude and statistical significance. Although as a multivariate test, sample size recommendations generally specify a larger number of observations than tested here, this procedure was utilized in this circumstance less as an inferential test and more as a metric of association. As outliers can have a large impact on the calculations, all cases were examined for extreme values using Mahalanobis distance. There were no outliers noted. 3.6 Grouping individuals based on performance An examination of the data across the 10 batteries revealed that participants exhibited individual differences in their vulnerability to sleep deprivation. As a means to stratify the wide variability in performance results into groups with similar characteristics, %correct and RT for the 3C-VT, %correct for the PAL and time awake during the modified MWT were converted to ordinal values. Table 2 presents the thresholds that were applied to assign ordinal values. Individuals were assigned to one of 3 groups by applying the fast-cluster routine in SAS (Release 8.01) to the transformed ordinal values across all 10 batteries. Thirteen participants were assigned to the least susceptible group (Low), 7 to the moderately susceptible group (Moderate), and 4 to the group highly susceptible to sleep deprivation (High). Cluster analysis was selected because it is a commonly used and accepted statistical procedure that could be replicated using other data sets.



The means and standard deviations for the RT and %Drowsy during the 3C-VT were calculated for each of the three cluster groups. Repeated measures analysis of variance (ANOVA) was performed using the assigned cluster group as a between participants variable. Probability values reported are based on the Greenhouse-Geisser corrected degrees of freedom. One-way ANOVAs were performed for RT and B-Alert %Drowsy at each of the 10 time-points to determine if there were significant differences for between the cluster groups.

3C-VT % Correct 3C-VT RT PAL % Correct MWT time Awake Ordinal Scale > < > < > < > <

0 95% 0.55 95% 30 40 1 90% 95% 0.55 0.65 90% 95% 20 30 2 85% 90% 0.65 0.75 85% 90% 10 20 3 75% 85% 0.75 0.85 75% 85% 10 4 65% 75% 0.85 0.95 65% 75% 5 65% 0.95 65%

Table 2: Conversion of performance measures to ordinal scales for each session.

4. RESULTS 4.1 Measuring drowsiness across 10 batteries Canonical correlations for individual participants are presented in Table 3. The average canonical correlation between B-Alert EEG and task performance variables was 0.889, and between EEG and technician scoring variables was 0.904. The canonical correlations indicate that there is a strong correlation between the B-Alert EEG classifications and the task performance or technician-observed drowsiness during the 3C-VT for most of the participants. Decrements in participant performance and technician-observed drowsiness are clearly associated with an increase in B-Alert classification as sleep-onset and drowsy.

Participant No.

EEG vs. Task Performance

EEG vs. Tech scoring

Participant No.

EEG vs. Task Performance

EEG vs. Tech scoring

1 0.946* NA 15 0.969* 0.997* 2 0.921* NA 16 0.802 0.960 3 0.929 NA 18 0.680 0.702 4 0.980* NA 19 0.948* 0.715 5 0.753 0.605 20 0.982* 0.973* 7 0.847 0.904 21 0.829 0.942* 8 0.924* 0.984* 22 0.974* 0.995* 9 0.638 0.973 23 0.974* 0.993*

11 0.911 0.931 24 0.790 0.852 12 0.982* NA 25 0.958* 0.994* 13 0.935 0.997* 26 0.740 0.755 14 0.933* NA 27 0.990* 0.997*

* = p < 0.05, NA = no data Table 3: Individual canonical correlations during the 3C-VT for EEG vs. performance and EEG vs. technician scoring. Repeated measures ANOVA across the 10 time-points revealed progressively increasing drowsiness over time as a result of sleep deprivation (Figure 2). The main effect for time was significant for all indices: B-Alert %High Vigilance (F=8.14, p<0.001) and %Drowsy (F=15.33, p<0.001), %correct (F=27.02, p<0.001), RT (F=40.13, p<0.001), , technician-observed %Fully Awake (F=15.91, p<0.001) and %Drowsy (F=11.86, p<0.001) during 3C-VT, %correct during PAL (F=17.57, p<0.001), and minutes awake during the modified MWT (F=21.47, p<0.001). These measure stabilized at the batteries occurring between 0600 and 1100 suggesting a circadian effect similar to those reported in previous sleep deprivation studies61-63.



The nap was followed by a transient period of decreased drowsiness in all indices. During the final battery, behavioral and physiological measures of drowsiness returned to levels prior to the nap. Lapses during the PVT-192 exhibited a pattern similar to the B-Alert classification, performance, and technician observations during the 3C-VT and PAL.

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napFig. 2.a. Fig. 2.b.

Fig. 2.c. Fig. 2.d.

Fig. 2.e. Fig. 2.f.

Figure 2: Repeated measures across 10 test batteries (mean + SE): a. B-Alert EEG % high vigilance and % drowsy classifications, b. % correct and reaction time during 3C-VT, c. Technician observations as fully awake or drowsy, d. % correct during PAL, e. Minutes awake during modified MWT, f. Lapses during PVT-192. X-axis provides the time each test was administered. All * indicate p < 0.01.



4.2 Individual differences in ability to cope with sleep deprivation Table 4 lists the results of the cluster analysis and the sum of the transformed ordinal values across all 10 batteries used to assign each participant into a sleep deprivation vulnerability group. The degree of susceptibility to sleep deprivation as measured by the sum of the ordinal values was normally distributed.

Low Moderate High Subj No. Ordinal Sum Subj No. Ordinal Sum Subj No. Ordinal Sum Subj No. Ordinal Sum

14 10 16 33 23 49 8 89 9 11 26 37 11 59 2 92 5 16 13 39 20 62 27 92

19 18 15 42 1 65 12 98 7 22 3 51 24 65

18 23 21 52 4 76 22 28 25 90

Table 4: Sum of ordinal values used to assign participants into sleep deprivation vulnerability groups (low, moderate, high). During the 44-hour sleep deprivation study, distinct patterns in the EEG and performance measures were associated with different levels of vulnerability to sleep deprivation (Figure 3).

10 x 20-min 3C-VT Across 44 Hours of Sleep Deprivation Stratified by Vulnerability Group (n=24)

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Figure 3. Mean 3C-VT RT and % drowsy for 3 groups stratified based on vulnerability to sleep deprivation. A 3(Group) x 10(Time-points) repeated measures ANOVA revealed significant Group x Time interactions for B-Alert %Drowsy (F = 2.58, p<0.05) and reaction time during the 3C-VT (F = 7.16, p<0.001). For each of the time-points, one-way ANOVAs were performed for B-Alert %Drowsy and reaction time to assess behavioral and physiological distinctions between groups. Significant differences between groups were found for B-Alert %Drowsy (F = 5.75, p<0.01) and reaction time (F = 34.62, p<0.001) beginning at the third evening battery at 0300Saturday, suggesting that these objective measures can provide a “biobehavioral assay” to identify individuals most susceptible to sleep deprivation. The mean subjective measures of sleepiness were also calculated for each group, but they did not reflect the level of drowsiness as measured by behavioral and physiological indices, suggesting that the participants’ perception of their own level of fatigue did not correspond to the objective measures. These data confirm previous studies documenting the limited reliability of subjective report31,64,65.



5. CONCLUSIONS

Until recently, sleep deprivation was believed to exert effects on human performance in a dose-dependent manner with each hour of sleep debt resulting in an equivalent and predictable amount of performance deficit20,21. A growing number of studies have now suggested that individuals may differ in their vulnerability to the effects of sleep deprivation25-32. Previously reported sleep deprivation studies suggested the possible existence of three groups of participants in the population: those extremely vulnerable to the effects of sleep deprivation, a moderately vulnerable group and a group relatively invulnerable to sleep deprivation30,66. However, this stratification is based on small sample sizes and the possibility exists that the level of vulnerability is normally distributed across the population. Whether these differences are stable over time (“traits”) or are associated with demographic (e.g. age, sex, race) or other (e.g. education, I.Q., socioeconomic status) characteristics remains to be determined. A genetic basis for the susceptibility to sleep deprivation may ultimately be identified. The B-Alert EEG classifications were highly correlated with technician-observed evidence of drowsiness, visual inspection of the EEG, and performance measures of alertness and memory. The B-Alert EEG classifications, 3C-VT and PAL performance were highly sensitive to the effects of sleep deprivation in the healthy participants and provided confirmation of the previously reported observation30,67 that individuals differ in their vulnerability to the effects of sleep deprivation. The data suggest that a combination of the B-Alert classifications and neuro-behavioral measures can identify individuals whose performance is most susceptible to sleep deprivation. The fact that the subjective measures of sleepiness did not distinguish between the groups suggests that the participants’ perception of their own level of fatigue did not correspond to the objective measures65. It is particularly noteworthy that the B-alert and RT during 3C-VT allowed clear separation of the three groups by the third post-baseline test, after only about 8 hours of sleep deprivation. One of the potential weaknesses of this study was the use of self-reported sleep logs rather than actigraphy (or direct observation) to ensure that participants received adequate sleep during the week preceding the study. It is possible that some of the observed degradations in performance may have resulted from the accumulation of prior sleep debt that was not recorded accurately in the sleep logs. Subsequent studies conducted by the investigators employed wrist actigraphs to verify sleep logs and have confirmed the existence of individual differences in susceptibility to the effects of sleep deprivation68. Ideally, the results of the study would be replicated with a much larger sample size. In addition, it would be of interest to continue to evaluate the same individuals over timeframes of two to five years to determine the stability and reliability of the sleep deprivation susceptibility effect. The question of what performance parameters are of greatest utility in sleep deprivation experiments and the methods used to quantify sleepiness is a topic of interest to many investigators. The assessment protocols used in the present study were selected for their sensitivity to even subtle shifts from alertness to drowsiness. Concurrent validity of the measures used in this study (i.e., 3C-VT, PAL, and modified MWT) was established in previous sleep deprivation studies by correlation with: a) behavioral evidence as measured by cessation of finger tapping, b) visually scored observations of facial signs of drowsiness (eye closures, head nods), c) responses to a subjective sleepiness questionnaire, d) visually scored EEG, e) modified maintenance of wakefulness test, f) handheld PVT-192 test and f) performance in a driving simulator47,56,57. If the results described above are confirmed with repeated measures in a larger sample of participants, it is possible that a combination of RT and B-alert classification during the 3C-VT could be used as a practical measure to identify individuals who are particularly susceptible or particularly resistant to sleep deprivation, based on only minimal sleep loss challenge. The system could offer a convenient and inexpensive screen to determine the suitability of employees for safety-sensitive positions requiring extended periods of wakefulness. The ability to select individuals who are relatively resistant to performance decrements due to sleep loss could have immense value to the military and the transportation industry.

ACKNOWLEDGEMENTS This research was supported by NIH NINDS grant number R43-NS35387 and contract number N43-NS72367.



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