Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy

Casdagli, Martin; Iasemidis, Leonidas D.; Savit, Robert; Gilmore, Robin L.; Roper, Steven N.; Sackellares, J. Chris

doi:10.1016/s0921-884x(96)95195-4

Cited by 157 publications

(69 citation statements)

References 12 publications

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“…Nonlinear parameters, such as the windowed correlation dimensions and complexity loss measure, are used to characterize the primary epileptogenic area in [22,23], respectively. The EEG recordings from seizure-free intervals can also be used to study the changes in the underlying dynamics of the cortex affected by epilepsy [24][25][26][27][28]. The focal and non-focal EEG signals are recorded from the patients affected by focal epilepsy.…”

Section: Introductionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

280

173

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The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn Avg ), average Renyi's entropy (RenEn Avg ), average approximate entropy (ApEn Avg ), average sample entropy (SpEn Avg ) and average phase entropies (S1 Avg and S2 Avg ), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

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Section: Introductionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

280

173

View full text Add to dashboard Cite

show abstract

“…Apart from physiologically based models, abstract models are also being developed that consider the brain as a physical dynamical system without detailed assumptions about the elements that constitute it ( [169] - [172]). Such models although not providing direct insight into neurophysiological mechanisms can provide a common conceptual framework and may reveal unifying principles, by showing e.g., that various seemingly different complex phenomena are governed by the same simple rules.…”

Section: Discussionmentioning

confidence: 99%

Some Insights Into Computational Models of (Patho)physiological Brain Activity

et al. 2006

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-The amount of experimental data concerning physiology and anatomy of the nervous system is growing very fast, challenging our capacity to make comprehensive syntheses of the plethora of data available. Computer models of neuronal networks provide useful tools to construct such syntheses. They can be used to interpret experimental data, generate experimentally testable predictions and formulate new hypotheses regarding the function of the neural systems. Models can also act as a bridge between different levels of neuronal organization. The ultimate aim of computational neuroscience is to provide a link between behavior and underlying neural mechanisms. Depending on the specific aim of the model, there are different levels of neuronal organization at which the model can be set. Models are constructed at the microscopic (molecular and cellular), macroscopic level (local populations or systems) or dynamical systems level. Apart from purely computational models, hybrid networks are being developed in which biological neurons are connected in vitro to computer simulated neurons. Also, neuromorphic systems are recently being created using silicon chips that mimic computational operations in the brain. This article reviews various computational models of the brain and insights obtained through their simulations.Index Terms-Brain modeling, computational neuroscience, nervous system, simulation

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“…The stationarity of EEG prior to a seizure may itself be an interesting phenomenon to study since it may be suggestive of changes in the state of the system. It is already known that there are changes in the EEG, which takes place from minutes to several hours prior to seizure onset [13]- [16]. To our knowledge there have been no comprehensive studies examining stationarity of EEG for large data sets.…”

Section: B Stationarity Of Preseizure Eegmentioning

confidence: 99%

Time-frequency spectral estimation of multichannel EEG using the Auto-SLEX method

Cranstoun

Ombao²,

Sachs³

et al. 2002

IEEE Trans. Biomed. Eng.

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Abstract-In this paper, we apply a new time-frequency spectral estimation method for multichannel data to epileptiform electroencephalography (EEG). The method is based on the smooth localized complex exponentials (SLEX) functions which are time-frequency localized versions of the Fourier functions and, hence, are ideal for analyzing nonstationary signals whose spectral properties evolve over time. The SLEX functions are simultaneously orthogonal and localized in time and frequency because they are obtained by applying a projection operator rather than a window or taper. In this paper, we present the Auto-SLEX method which is a statistical method that 1) computes the periodogram using the SLEX transform, 2) automatically segments the signal into approximately stationary segments using an objective criterion that is based on log energy, and 3) automatically selects the optimal bandwidth of the spectral smoothing window. The method is applied to the intracranial EEG from a patient with temporal lobe epilepsy. This analysis reveals a reduction in average duration of stationarity in preseizure epochs of data compared to baseline. These changes begin up to hours prior to electrical seizure onset in this patient.

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Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy

Cited by 157 publications

References 12 publications

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Some Insights Into Computational Models of (Patho)physiological Brain Activity

Time-frequency spectral estimation of multichannel EEG using the Auto-SLEX method

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