Estimation of the Correlation Dimension of All-Night Sleep EEG Data with a Personal Super Computer

1994 ◽  
pp. 283-290 ◽  
Author(s):  
Peter Achermann ◽  
Rolf Hartmann ◽  
Anton Gunzinger ◽  
Walter Guggenbühl ◽  
Alexander A. Borbély
Keyword(s):  
Correlation Dimension ◽  
Sleep Eeg ◽  
Night Sleep ◽  
Eeg Data ◽  
Super Computer

Effect of Ethanol on Human Sleep EEG Using Correlation Dimension Analysis

2002 ◽  
Vol 46 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Toshio Kobayashi ◽  
Shigeki Madokoro ◽  
Yuji Wada ◽  
Kiwamu Misaki ◽  
Hiroki Nakagawa
Keyword(s):  
Correlation Dimension ◽  
Sleep Eeg ◽  
Human Sleep ◽  
Dimension Analysis

scholarly journals QUANTIFICATION OF DEPTH OF ANESTHESIA BY MEANS OF ADAPTIVE CALCULATION OF CORRELATION DIMENSION PARAMETERS

Fractals ◽  
2009 ◽  
Vol 17 (04) ◽  
pp. 473-483
Author(s):  
BEHZAD AHMADI ◽  
BAHAREH ZAGHARI ◽  
RASSOUL AMIRFATTAHI ◽  
MOJTABA MANSOURI
Keyword(s):  
Correlation Dimension ◽  
Single Channel ◽  
Drug Effects ◽  
Eeg Signal ◽  
Depth Of Anesthesia ◽  
Anesthetic Drugs ◽  
Eeg Data ◽  
Prediction Probability ◽  
Analysis Application ◽  
Electroencephalogram Eeg

This paper proposes an approach for quantifying Depth of Anesthesia (DOA) based on correlation dimension (D2) of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room while different anesthetic drugs, including propofol and isoflurane, were used. Correlation dimension was computed using various optimized parameters in order to achieve the maximum sensitivity to anesthetic drug effects and to enable real time computation. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA was evaluated and compared to fixed segmentation, too. Prediction probability (PK) was used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. Appropriate correlation between DOA and correlation dimension is achieved while choosing (D2) parameters adaptively in comparison to fixed parameters due to the nonstationary nature of EEG signal.

QRS artifact elimination on full night sleep EEG

2006 ◽  
Vol 28 (2) ◽  
pp. 156-165 ◽  
Author(s):  
J.-P. Lanquart ◽  
M. Dumont ◽  
P. Linkowski
Keyword(s):  
Sleep Eeg ◽  
Night Sleep

Expert-in-the-loop Learning for Sleep EEG Data

2018 ◽  
Author(s):  
Vaclav Gerla ◽  
Vaclav Kremen ◽  
Martin Macas ◽  
Elizaveta Saifutdinova ◽  
Arnost Mladek ◽  
...  
Keyword(s):  
Sleep Eeg ◽  
Eeg Data

On the Use of Neural Network Techniques to Analyse Sleep EEG Data

1997 ◽  
Vol 36 (4) ◽  
pp. 194-210 ◽  
Author(s):  
R.B. Baumgart-Schmitt ◽  
W.M. Herrmann ◽  
R. Eilers ◽  
F. Bes
Keyword(s):  
Neural Network ◽  
Sleep Eeg ◽  
Eeg Data

Human Sleep EEG Analysis Using the Correlation Dimension

2001 ◽  
Vol 32 (3) ◽  
pp. 112-118 ◽  
Author(s):  
Toshio Kobayashi ◽  
Shigeki Madokoro ◽  
Yuji Wada ◽  
Kiwamu Misaki ◽  
Hiroki Nakagawa
Keyword(s):  
Rem Sleep ◽  
Correlation Dimension ◽  
Degrees Of Freedom ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Cycle ◽  
Sleep Stages ◽  
The Mean ◽  
Male Subjects

Sleep electroencephalograms (EEG) were analyzed by non-linear analysis. Polysomnography (PSG) of nine healthy male subjects was analyzed and the correlation dimension (D2) was calculated. The D2 characterizes the dynamics of the sleep EEG, estimates the degrees of freedom, and describes the complexity of the signal. The mean D2 decreased from the awake stage to stages 1,2,3 and 4 and increased during rapid eye movement (REM) sleep. The D2 during each REM sleep stage were high and those during each slow wave sleep stage were low, respectively, for each sleep cycle. The mean D2 of the sleep EEG in the second half of the night was significantly higher than those in the first half of the night. Significant changes were also observed during sleep stage 2, but were not seen during REM sleep and sleep stages 3 and 4. The D2 may be a useful method in the analysis of the entire sleep EEG.

scholarly journals Assessment of potential capabilities of Dreem: An ambulatory device for EEG phase-locked acoustic stimulation during sleep

2019 ◽  
Vol 9 (1) ◽  
pp. 96-112
Author(s):  
A.N. Puchkova ◽  
O.N. Tkachenko ◽  
I.P. Trapeznikov ◽  
I.A. Piletskaya ◽  
E.V. Tiunova ◽  
...  
Keyword(s):  
Slow Wave ◽  
Brain Activity ◽  
Modern Society ◽  
Acoustic Stimulation ◽  
Sleep Eeg ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Night Sleep ◽  
Delta Wave ◽  
Photo Sensor

Sleep disorders are one of the significant problems in the modern society. Current research is on the lookout for the nonpharmacological ways to improve sleep quality and slow wave brain activity that plays a crucial role in homeostasis and cognitive functions. One of the promising approaches is acoustic stimulation that is phase-locked to deep sleep EEG rhythms. It was already shown that such stimulation improves slow wave brain activity. This article describes Dreem: a wireless consumer device that performs acoustic sleep stimulation in home conditions. The device has dry EEG electrodes, photo sensor for pulse oximetry, and an accelerometer. The inbuilt software detects deep sleep, performs audio stimulation on the ascending slope of the delta wave and does automatic sleep staging. In the pilot study of the device, three subjects made 10 to 24 recordings of night sleep with EEG recording and stimulation. The raw data recorded by the device is available to the user and is sufficient for sleep staging and basic sleep analysis. Automatic hypnograms reflect the structure of a normal night sleep. EEG averaged by the stimulation markers demonstrated the high efficacy of slow wave detectors and placement of stimulations on the ascending slope of a delta wave. Dreem device is of interest for the sleep researchers as an easy to use tool for an out-of-lab data acquisition.

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