When does this cortical area drop off? Principal component structuring of the EEG spectrum yields yes-or-no criteria of local sleep onset

How to quantify sleepiness during an attempt to sleep?

Background. Sleepiness assessment tools were mostly developed for detection of an elevated sleepiness level in the condition of sleep deprivation and several medical conditions. However, sleepiness occurs in various other conditions including the transition from wakefulness to sleep during an everyday attempt to get sleep.Objective. We examined whether objective sleepiness indexes can be implicated in detection of fluctuations in sleepiness level during the polysomnographically-monitored attempt to sleep, i.e. in the absence of self-reports on perceived sleepiness level throughout such an attempt.Approach. The polysomnographic signals were recorded in the afternoon throughout 106 90 min napping attempts of 53 university students (28 females). To calculate two objective sleepiness indexes, the electroencephalographic (EEG) spectra were averaged on 30 s epochs of each record, assigned to one of 5 sleep-wake stages, and scored using either the frequency weighting curve for sleepiness substate of wake state or loadings of each frequency on the 2nd principal component of variation in the EEG spectrum (either sleepiness score or PC2 score, respectively).Main results. We showed that statistically significant fluctuations in these two objective sleepiness indexes during epochs assigned to wake stage can be described in terms of the changes in verbally anchored levels of subjective sleepiness assessed by scoring on the 9-step Karolinska Sleepiness Scale.Significance. The results afford new opportunities to elaborate importance of intermediate substates between wake and sleep states for sleep-wake dynamics in healthy individuals and patients with disturbed sleep.

Linking stages of non-rapid eye movement sleep to the spectral EEG markers of the drives for sleep and wake

The conventional staging classification reduces all patterns of sleep polysomnogram signals to a small number of yes-or-no variables labeled wake or a stage of sleep (e.g., W, N1, N2, N3, and R for wake, the first, second, and third stages of non-rapid eye movement sleep and rapid eye movement sleep, respectively). However, the neurobiological underpinnings of such stages remained to be elucidated. We tried to evaluate their link to scores on the first and second principal components of the EEG spectrum (1PCS and 2PCS), the markers of two major groups of promoters/inhibitors of sleep/wakefulness delineated as the drives for sleep and wake, respectively. On two occasions, polysomnographic records were obtained from 69 university students during 50-min afternoon naps and 30-s stage epochs were assigned to 1PCS and 2PCS. Results suggested two dimensionality of the structure of individual differences in amounts of stages. Amount of N1 loaded exclusively on one of two dimensions associated with 1PCS, amounts of W and N2 loaded exclusively on another dimension associated with 2PCS, and amount of N3 was equally loaded on both dimensions. Scores demonstrated stability within each stage, but a drastic change in just one of two scores occurred during transitions from one stage to another on the way from wakefulness to deeper sleep (e.g., 2PCS changed from >0 to <0 during transition W→N1, 1PCS changed from <0 to >0 during transition N1→N2). Therefore, the transitions between stages observed during short naps might be linked to rapid changes in the reciprocal interactions between the promoters/inhibitors of sleep/wakefulness.NEW & NOTEWORTHY In the present nap study, two dimensionality of the structure of individual differences in sleep stages was revealed. These results also suggested that individual variation in the sleep and wake drives associated with the first and second principal components of the EEG spectrum might underlie this structure. It seemed that each stage might be related to a certain, stage-specific combination of wake-sleep promoting/inhibiting influences associated with these drives for sleep and wake.

Generalizability of Frequency Weighting Curve for Extraction of Spectral Drowsy Component From the EEG Signals Recorded in Eyes-Closed Condition

One of the critical barriers to reducing the threats of sleep loss to public health, safety, and productivity is a lack of practical tools for quick identification of objective level of sleepiness. We examined a novel sleepiness measure named "spectral drowsy component score" to provide evidence for generalizability of a frequency weighting curve required for calculation of this measure. Each spectral drowsy component score is a sum of 16 weighted ln-transformed single-Hz power densities (1-16 Hz) obtained by the fast Fourier transformation of an electroencephalographic signal recorded during the first minute after closing the eyes. A set of 16 weights (frequency weighting curve) is derived empirically. One type of such curve is a correlation spectrum. It consists of 16 coefficients of correlation of a group-averaged experimental time course of sleepiness with16 time courses of single-Hz power densities. Sleepiness is determined either subjectively (by self-scoring on the Karolinska Sleepiness Scale) or objectively (as sleep latency). Another type is a differential spectrum reflecting difference between 2 sets of 16 power densities obtained for either distant phases of sleep deprivation experiment or distinct alertness-sleepiness substates. Analysis of 3 datasets collected in sleep deprivation experiments with, in total, 160 participants showed that, despite differences in the protocols of these experiments and ages of their participants, the forms of frequency weighting curves always resembled one another. Such resemblance led to practical identity of scoring results. We concluded that spectral drowsy component scoring might be implemented into quick, simple, direct, transparent, and objective test of sleepiness.

Principal component analysis of the EEG spectrum can provide yes-or-no criteria for demarcation of boundaries between NREM sleep stages

Human sleep begins in stage 1 and progresses into stages 2 and 3 of Non-Rapid-Eye-Movement (NREM) sleep. These stages were defined using several arbitrarily-defined thresholds for subdivision of albeit continuous process of sleep deepening. Since recent studies indicate that stage 3 (slow wave sleep) has unique vital functions, more accurate measurement of this stage duration and continuity might be required for both research and practical purposes. However, the true neurophysiological boundary between stages 2 and 3 remains unknown. In a search for non-arbitrary threshold criteria for distinguishing the boundaries between NREM sleep stages, scores on the principal components of the electroencephalographic (EEG) spectrum were analyzed in relation to stage onsets. Eighteen young men made 12-20-minute attempts to nap during 24-hour wakefulness. Single-minute intervals of the nap EEG records were assigned relative to the minute of onsets of polysomnographically determined stages 1, 2, and 3. The analysis of within-nap time courses of principal components scores revealed that, unlike any conventional spectral EEG index, score on the 4th principal component exhibited a rather rapid rise on the boundary between stages 2 and 3. This was mostly a change from negative to positive score. Therefore, it might serve as yes-or-no criterion of stage 3 onset. Additionally, similarly rapid changes in sign of scores were exhibited by the 1st and 2nd principal components on the boundary of stages 2 and 1 and on the boundary between stage 1 and wakefulness, respectively.

Calibration of an objective alertness scale

In order to establish validity of an objective scale of alertness-sleepiness, one is forced to somehow calibrate it using subjective scales, such as the Karolinska Sleepiness Scale (KSS). We evaluated the effects of prolonged wakefulness on the extent of disagreement between objective and subjective KSS assessments, and tested whether calibration of an objective alertness-sleepiness scale can be established despite the limited reliability of subjective reports. Starting from 7p.m., the resting electroencephalogram (EEG) was recorded at 2-hour intervals over the last 32-50 h of 44-61-hour wakefulness of 15 healthy study participants. Frontal and occipital scores on the 2nd principal component of the EEG spectrum and occipital alpha-theta power difference were computed for 1-min intervals of 5-min eyes-closed EEG recordings. To obtain alertness scale scores in the range from 5 to 0, positive and negative values of these EEG indexes were assigned to 1 and 0, respectively, and then summed. Although correlation between time courses of objective and subjective (KSS) scores was very strong, evidence for systematic errors in both the mean and the calibration was also found. Correction of these errors resulted in strengthening of correlation (r = 0.99) and establishing one-to-one correspondence between the steps of objective and subjective scales. The results indicate that scores from 5 to 0 on the objective alertness scale can be anchored to minimal, mild, moderate, marked, severe, and disabling levels of sleepiness.