All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions

Achermann, Peter; Hartmann, Rolf; Gunzinger, Anton; Guggenbühl, W.; Borbély, Alexander A.

doi:10.1016/0013-4694(94)90054-x

Cited by 71 publications

(37 citation statements)

References 10 publications

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“…The pioneering studies used methods such as correlation dimension (D2) [48][49][50], Hurst exponent (H) [51] and detrended fluctuations analysis (DFA) [52][53][54]. Complexity of brain activity was quantified by a relative increase or decrease where D2 and H measures decreased from wakefulness to NREM sleep and increased during REM sleep.…”

Section: Discussionmentioning

confidence: 99%

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Tosun

Abásolo

Stenson

et al. 2017

Entropy

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Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG). Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS), i.e., wakefulness, rapid-eye movement (REM) and non-rapid-eye movement (NREM) sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel-Ziv complexity (PLZC), a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD). The results obtained with PLZC were contrasted with a related non-linear method, Lempel-Ziv complexity (LZC). Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.

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Section: Discussionmentioning

confidence: 99%

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Tosun

Abásolo

Stenson

et al. 2017

Entropy

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“…In terms of the model, all discussed properties will be presented. For electro-encephalogram (EEG) signals linear approximation is sufficient (except for a case of certain epochs of an epileptic seizure) as demonstrated by Pijn et al (1991Pijn et al ( , 1997, Stam et al (1999), Blinowska & Malinowski (1991) and Achermann et al (1994). The AR model assumes that a value of the signal x at time t depends on its previous values ( p) with a random component 3…”

Section: Methodsmentioning

confidence: 99%

Determination of transmission patterns in multichannel data

Kamiński

2005

Phil. Trans. R. Soc. B

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The power of today's laboratory equipment allows more and more data channels to be easily recorded. However, some misunderstandings about processing such multivariate data may still be found in the literature. The typical mistake is to treat a multichannel system as comprising pairs of channels; this approach does not use all the collected information about the investigated system, and may lead to erroneous conclusions. In this paper, the differences between single-and multichannel approaches will be briefly summarized and some examples of problems will be described.

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“…A high estimate indicates that the dynamic system is highly complicated and needs more elements to describe its properties. The existence of a chaotic attractor of EEG has been proposed because of the finding of low correlation dimensions [22]; however, a low correlation dimension is not sufficient evidence to prove the existence of a chaotic attractor because a ‘noise’ characterized by the f –α (α > 1) power spectral law may also yield a low dimension [24]. Therefore, surrogate data whose power spectrum is identical to that of original EEGs, but for which all information contained in the phase is removed have been proposed [24, 25].…”

Section: Introductionmentioning

confidence: 99%

“…The existence of a chaotic attractor of EEG has been proposed because of the finding of low correlation dimensions [22]; however, a low correlation dimension is not sufficient evidence to prove the existence of a chaotic attractor because a ‘noise’ characterized by the f –α (α > 1) power spectral law may also yield a low dimension [24]. Therefore, surrogate data whose power spectrum is identical to that of original EEGs, but for which all information contained in the phase is removed have been proposed [24, 25]. If correlation dimensions of EEG signals are clearly lower than those of surrogate data, the mechanism of neuronal networks underlying the EEG can be described by a nonlinear process [25, 26].…”

Section: Introductionmentioning

confidence: 99%

Relationship of Nonlinear Analysis, MRI and SPECT in the Lateralization of Temporal Lobe Epilepsy

Jing

Takigawa²,

Benasich³

2002

Eur Neurol

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Objectives: The purpose of this study was to investigate the correlation of lateralization by nonlinear analysis, magnetic resonance imaging (MRI) and interictal single-photon emission computed tomography (SPECT) in patients with temporal lobe epilepsy. Methods: Twenty-three patients (7 males, 16 females) were examined by MRI, interictal SPECT and EEG. Nonlinear dynamic properties of neuronal networks were estimated by calculating correlation dimensions on interictal EEG signals and corresponding surrogate data. Lateralization was detected based on the criteria introduced in this study. Concordance rates of the results among the three methods were compared. Results: Epileptogenic foci were shown in the temporal areas in 21 patients using the nonlinear method (8 left, 2 right, 11 both), while 20 patients showed abnormalities in temporal lobes on MR images (13 left, 5 right, 2 both). Low cerebral blood flows of the temporal lobes were detected in all patients (11 left, 8 right, 4 both). Completely concordant lateralization was observed in 8 patients (35%) for the nonlinear method and MRI, in 9 patients (39%) for the nonlinear method and SPECT, and in 10 patients (43%) for MRI and SPECT. There were no significant differences among the concordance rates for these different methods. Conclusions: Our results revealed that correlation dimension is useful for differentiating dynamic properties of neuronal networks in the interictal state, and can provide informative data for localizing epileptogenic foci in epileptic patients. Therefore, the present nonlinear method is recommended for use with patients during presurgical evaluation.

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All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions

Cited by 71 publications

References 10 publications

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Determination of transmission patterns in multichannel data

Relationship of Nonlinear Analysis, MRI and SPECT in the Lateralization of Temporal Lobe Epilepsy

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