Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification

Gifani, P.; Rabiee, Hamid R.; Hashemi, Mehrdad; Taslimi, Pejman; Ghanbari, M.

doi:10.1016/j.jfranklin.2006.08.004

Cited by 54 publications

(31 citation statements)

References 12 publications

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“…1, states A, B, C, D and E have BIS values of 15, 30, 50, 80 and 97, which are corresponding to very deep anaesthesia, deep anaesthesia, moderate anaesthesia, light anaesthesia and awake states. The source of the signals used in this paper is from and discussed in [12]. The raw EEG signals were recorded by BIS XP monitor (Aspect Medical System Inc.) through contact electrodes placed on a patient's forehead.…”

Section: Detrended Fluctuation Analysis Methodsmentioning

confidence: 99%

“…When the trend is found in each box, it is subtracted from the integrated signals. To investigate the effect of box sizes, [12] gives comprehensive reviews about the fractal and self-similarity properties of the EEG signals. It managed to find the optimum fractal-scaling exponent by selecting the best domain of box sizes, which have meaningful changes with different depth of anaesthesia.…”

Section: Introductionmentioning

confidence: 99%

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Improving the accuracy of depth of anaesthesia using modified detrended fluctuation analysis method

Nguyen-Ky

Wen

2010

Biomedical Signal Processing and Control

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Section: Detrended Fluctuation Analysis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of depth of anaesthesia using modified detrended fluctuation analysis method

Nguyen-Ky

Wen

2010

Biomedical Signal Processing and Control

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“…In addition, it is a scaling analysis method providing a simple quantitative parameter (scaling exponent) to represent the long-range correlation properties of a signal [14].…”

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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“…In this paper, we have proposed an optimal nonlinear analysis algorithm for processing the EEG signals without being concerned about the non-stationarity and finite length of the signal. The fractal-scaling exponents that quantify the powerlaw correlations are computed by DFA which is known for its robustness against nonstationarity in [6].…”

Section: Detrended Fluctuation Analysismentioning

confidence: 99%

“…DFA was invented by Peng [5] and established as an important tool for the detection of long-range (auto-) correlations in time series with non-stationarities. In [6] DFA is used to extract a depth of anesthesia index. It is also used in [7] to measure the depth of anesthesia.…”

Section: Introductionmentioning

confidence: 99%

Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis

Hosseini¹,

Shalbaf²,

Nasrabadi³

2010

JBiSE

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Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and loss of consciousness to blank staring, lip smacking, or jerking movements of arms and legs. If early warning signals of an upcoming seizure (diagnosis of preictal period) are detected, proper treatment can be applied to the patient to help prevent the seizure. In this research, an epileptic disorder has been divided into three subsets: Normal, Preictal (just before the seizure), and Ictal (during seizure). By using Detrended Fluctuation Analysis (DFA), Bispectral Analysis (BIS), and Standard Deviation (SD) three features from single-channel EEG signals have been derived in the foresaid groups. A fuzzy classifier is used to separate the three groups which can successfully separate them with a separation degree of 100% and further a fuzzy inference engine is used to extract a Seizure Intensity Index (SII) from the Electroencephalogram (EEG) signals of the three different states. One can apparently see the distinction of SII amounts between the three states. It is more important when one remembers that these results are just from single-channel EEG signal.

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Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification

Cited by 54 publications

References 12 publications

Improving the accuracy of depth of anaesthesia using modified detrended fluctuation analysis method

Improving the accuracy of depth of anaesthesia using modified detrended fluctuation analysis method

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

Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis

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