Continuous EEG-based dynamic markers for sleep depth and phasic events

Carrubba, Simona; Kim, Paul Young Young; McCarty, David E.; Chesson, Andrew L.; Frilot, Clifton; Marino, Andrew A.

doi:10.1016/j.jneumeth.2012.04.018

Cited by 18 publications

(19 citation statements)

References 12 publications

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“…The signal-processing techniques were developed to study nonlinear physical systems and subsequently extended to physiological signals [16,17], including the vigilant and sleep EEGs [9,[18][19][20]. Briefly, at time t a 5-component vector was formed that consisted of the EEG amplitude at t and four earlier times identified by four successive lags of five points (10 msec).…”

Section: Recurrence Analysismentioning

confidence: 99%

“…The Euclidean norm was used for measuring distance, and vectors were identified as near if they were within 15% of the distance between the two vectors that were furthest apart. These choices (and those for dimension and lag) were previously found to be useful for quantifying deterministic activity in the EEG [9,18,19].…”

Section: Recurrence Analysismentioning

confidence: 99%

“…Similarly, additional refinement of scoring arousals is needed [8]. We recently showed that a recurrence marker computed by algorithmic analysis of the EEG stratified all sleep stages, increased progressively with NREM sleep-stage depth (N1 b N2 b N3), and characterized sleep fragmentation caused by arousal events [9].…”

Section: Introductionmentioning

confidence: 99%

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EEG recurrence markers and sleep quality

Wang

Kim

McCarty

et al. 2013

Journal of the Neurological Sciences

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Section: Recurrence Analysismentioning

confidence: 99%

Section: Recurrence Analysismentioning

confidence: 99%

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EEG recurrence markers and sleep quality

Wang

Kim

McCarty

et al. 2013

Journal of the Neurological Sciences

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“…In another study [6], the sleep EEG analysis is carried out using non-linear parameters such as correlation dimension, fractal dimension, largest Lyapunov, entropy, approximate entropy, Hu rst exponent, phase space plot and recurrence plots. Emp loying recurrence analysis, Carrubba et al [25] developed a method for capturing and quantifying the dynamical states of the brain during sleep. Recently, Brignol et al [26] proposed a new phase space-based (main ly based on Poincaré plot) algorith m for automat ic classification of sleep-wake states in humans using short-time EEG data.…”

Section: Imentioning

confidence: 99%

Analysis of Electroencephalogram Signals in Different Sleep Stages using Detrended Fluctuation Analysis

Goshvarpour

Abbasi²

2013

IJIGSP

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-Scaling behavior is an indicator of the lack of characteristic time scale, and the existence of longrange correlations related to physiological constancy preservation. To investigate the fluctuations of the sleep electroencephalogram (EEG) over various time scales during different sleep stages detrended fluctuation analysis (DFA) is studied. The sleep EEG signals for analysis were obtained fro m the Sleep-EDF Database available online at the PhysioBank. The DFA computations were performed in different sleep stages. The scaling behavior of these time series was investigated with detrended fluctuation analysis (window size: 50 to 500). The results show that the mean values of scaling exponents were lo wer in subjects during stage 4 and standard deviation of scaling exponents of stage 4 was larger than that of the other stages. In contrast, the mean value of scaling exponents of stage 2 was larger, wh ile a small variat ion of scaling exponent is observed at this stage. Therefore, DFA has a more stable behavior in stage 2, whereas the random variability and unpredictable behavior of DFA can be observed in the stage 4. In conclusion, scaling exponent indices are efficacious in quantifying EEG signals in different sleep stages.

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“…Recently, using nonlinear analysis that quantified the recurrence properties of the EEG, Carrubba et al [14] described a novel method for producing dynamic markers of brain states during sleep.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Evaluation of Electroencephalogram Signals in Different Sleep Stages in Apnea Episodes

Goshvarpour¹,

Abbasi²,

Goshvarpour³

2013

IJISA

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Abstract-Distinct sleep phases are related to different dynamical patterns in electroencephalogram (EEG) signals. In this article, the relationship between the sleep stages and nonlinear behavior of sleep EEG is explored. In particular, analysis of approximate entropy (ApEn) and the largest Lyapunov exponent is evaluated in patients with sleep apnea, which is defined as respiratory flow that is suspended or decreased for more than 10 s. The pathological sleep EEG signals for analysis were obtained from the MIT-BIH polysomnography database available online at the PhysioBank. The results show that for the both normal and apneic sleep epochs, ApEn decreased significantly as the sleep goes into deeper stages. Therefore, it indicated that as sleep becomes deeper, the brain function becomes less activated. Compared with normal sleep, the mean value of largest lyapunov exponents was also significantly lower than that of normal epochs during deep sleep stages. The results also show that the average largest lyapunov exponents of EEG signals increased in the REM state. Because during this stage of sleep, the cortex becomes more active and more neurons incorporate in the information processing. In conclusion, the nonlinear dynamical measures obtained from the nonlinear dynamical analysis such as the approximate entropy and largest lyapunov exponents can be useful for characterizing the physiological or pathological states of the brain.

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Continuous EEG-based dynamic markers for sleep depth and phasic events

Cited by 18 publications

References 12 publications

EEG recurrence markers and sleep quality

EEG recurrence markers and sleep quality

Analysis of Electroencephalogram Signals in Different Sleep Stages using Detrended Fluctuation Analysis

Nonlinear Evaluation of Electroencephalogram Signals in Different Sleep Stages in Apnea Episodes

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