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

Putilov AA - Sleep Sci (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Research Institute for Molecular Biology and Biophysics, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk, Russia.

ABSTRACT
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.

No MeSH data available.


Loading spectra of the four largest principal components of the EEG spectrum. Loading spectra for the 1st–4th principal components (frequency range between 1 and 16 Hz) were calculated for the whole data set (A) and separately for each of 18 study participants (B). Individual loadings were then averaged drawn as Mean loading±95% Confedence Interval.
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f0005: Loading spectra of the four largest principal components of the EEG spectrum. Loading spectra for the 1st–4th principal components (frequency range between 1 and 16 Hz) were calculated for the whole data set (A) and separately for each of 18 study participants (B). Individual loadings were then averaged drawn as Mean loading±95% Confedence Interval.

Mentions: The one-min spectra calculated for each 20-min nap were assigned relative to 0-minutes of polysomnographically determined onset of stage 1 (N1), stage 2 (N2), and stage 3 (N3). In total, 164 naps were used for the present analysis after exclusion of a few naps either containing REM sleep or showing that NREM sleep was interrupted by wakefulness. Spectra on the interval of the first 16 power values (the range from 1 to 16 Hz) were log-transformed and subjected to principal component analysis. Additionally, the log-transformed power values were averaged over frequency ranges roughly corresponding to delta, theta, alpha, and sigma activities (1–4, 5–8, 9–12, and 13–16 Hz, respectively). The SPSS statistical software package, version 21, was used for all statistical analyses (SPSS, Chicago, IL). Principal component analysis was run either on all sets of spectra or on the sets obtained from each of 18 participants (Fig. 1A and B). Each set was decomposed into four principal component scores. In order to calculate a score on each of the four components, the 16 original power values were optimally weighted in accord with their loadings on this component (Fig. 1A) and then summed. More details regarding the methodology of principal component analysis of the EEG spectrum have been reported earlier [11,12,17,18].


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

Putilov AA - Sleep Sci (2015)

Loading spectra of the four largest principal components of the EEG spectrum. Loading spectra for the 1st–4th principal components (frequency range between 1 and 16 Hz) were calculated for the whole data set (A) and separately for each of 18 study participants (B). Individual loadings were then averaged drawn as Mean loading±95% Confedence Interval.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608893&req=5

f0005: Loading spectra of the four largest principal components of the EEG spectrum. Loading spectra for the 1st–4th principal components (frequency range between 1 and 16 Hz) were calculated for the whole data set (A) and separately for each of 18 study participants (B). Individual loadings were then averaged drawn as Mean loading±95% Confedence Interval.
Mentions: The one-min spectra calculated for each 20-min nap were assigned relative to 0-minutes of polysomnographically determined onset of stage 1 (N1), stage 2 (N2), and stage 3 (N3). In total, 164 naps were used for the present analysis after exclusion of a few naps either containing REM sleep or showing that NREM sleep was interrupted by wakefulness. Spectra on the interval of the first 16 power values (the range from 1 to 16 Hz) were log-transformed and subjected to principal component analysis. Additionally, the log-transformed power values were averaged over frequency ranges roughly corresponding to delta, theta, alpha, and sigma activities (1–4, 5–8, 9–12, and 13–16 Hz, respectively). The SPSS statistical software package, version 21, was used for all statistical analyses (SPSS, Chicago, IL). Principal component analysis was run either on all sets of spectra or on the sets obtained from each of 18 participants (Fig. 1A and B). Each set was decomposed into four principal component scores. In order to calculate a score on each of the four components, the 16 original power values were optimally weighted in accord with their loadings on this component (Fig. 1A) and then summed. More details regarding the methodology of principal component analysis of the EEG spectrum have been reported earlier [11,12,17,18].

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Research Institute for Molecular Biology and Biophysics, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk, Russia.

ABSTRACT
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.

No MeSH data available.