scholarly journals Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

1997 ◽  
Vol 9 (4) ◽  
pp. 249-270 ◽  
Author(s):  
Jan Pieter M. Pijn ◽  
Demetrios N. Velis ◽  
Marcel J. van der Heyden ◽  
Jaap DeGoede ◽  
Cees W. M. van Veelen ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Epileptic Seizures ◽  
Intracranial Eeg ◽  
Eeg Recordings

scholarly journals Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain

2016 ◽  
Author(s):  
Adrià Tauste Campo ◽  
Alessandro Principe ◽  
Miguel Ley ◽  
Rodrigo Rocamora ◽  
Gustavo Deco
Keyword(s):  
Epileptic Seizures ◽  
Time Dependent ◽  
Clear Understanding ◽  
Intracranial Eeg ◽  
Critical Phase ◽  
Prediction And Control ◽  
Eeg Recordings ◽  
Highly Correlated ◽  
Network States ◽  
And Control

AbstractEpileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide cross-validated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial EEG recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable (“degenerate”) and is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to particularly affect the activity of resection regions in patients with validated postsurgical outcome. Our approach characterizes pre-seizure networks dynamics as a cascade of two sequential events providing new insights into seizure prediction and control.

scholarly journals Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jan Pyrzowski ◽  
Jean- Eudes Le Douget ◽  
Amal Fouad ◽  
Mariusz Siemiński ◽  
Joanna Jędrzejczak ◽  
...  
Keyword(s):  
Normal Subjects ◽  
Intracranial Eeg ◽  
Zero Crossing ◽  
Temporal Precision ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Eeg Recordings ◽  
Patients With Epilepsy ◽  
Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Physiological Activity and Artefacts in Epileptic Brain in Subdural EEG

2018 ◽  
pp. 84-97
Author(s):  
Beate Diehl ◽  
Catherine A. Scott
Keyword(s):  
Motor Cortex ◽  
Brain Region ◽  
Physiological Activity ◽  
Brain Oscillations ◽  
Intracranial Eeg ◽  
Scalp Eeg ◽  
Focal Pattern ◽  
Mu Rhythms ◽  
Eeg Recordings ◽  
Central Pacemaker

‘Physiological activity and artefacts in epileptic brain in subdural EEG’ reviews intracranial appearances of physiological brain rhythms in each brain region, many of which are also seen on scalp EEG. The alpha rhythm has been described as originating from multiple occipital and extra-occipital cortical generators variously overlapping and influencing each other, probably under the relative control of a central pacemaker. Another more focal pattern has been described in intracranial EEG recordings in the calcarine region, with a third rhythm arising in midtemporal regions, not detectable in scalp EEG, with a frequency in the alpha or theta range. Lambda waves, sleep structures, and mu rhythms over motor cortex can also be detected on subdural electrodes. On a region-by-region basis, intracranial EEG appearances are summarized, including brain oscillations in hippocampus and motor cortex and their modifiers, as well as ongoing rhythms in cingulum. Common sources of physiological and non-physiological artefacts are reviewed.

Roughness-Length-Based Characteristic Analysis of Intracranial EEG and Epileptic Seizure Prediction

2020 ◽  
Vol 30 (12) ◽  
pp. 2050072
Author(s):  
Yanli Zhang ◽  
Rendi Yang ◽  
Weidong Zhou
Keyword(s):  
Fractal Dimension ◽  
Roughness Length ◽  
Epileptic Seizures ◽  
Prediction Algorithm ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Seizure Prediction ◽  
Characteristic Analysis ◽  
Intracranial Eeg ◽  
Interictal Eeg

To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those in the interictal phase, a patient-specific seizure prediction algorithm is then proposed by combining with a gradient boosting classifier. The probabilistic outputs of the trained gradient boosting classifier are further processed by threshold comparison and rule-based judgment to distinguish preictal EEG from interictal EEG and to generate seizure alerts. The prediction algorithm was evaluated on 20 patients’ intracranial EEG recordings from the Freiburg EEG database, which contains the preictal periods of 65 seizures and 499[Formula: see text]h interictal EEG. Setting the seizure prediction horizon as 2[Formula: see text]min, averaged sensitivity values of 90.42% and 91.67% with averaged false prediction rates of 0.12/h and 0.10/h were achieved for seizure occurrence periods of 30 and 50[Formula: see text]min, respectively. These results demonstrate the ability of fractal dimension and intercept metrics in predicting the occurrence of seizures.

An Efficient Approach to Define the Input Stimuli to Suppress Epileptic Seizures Described by the Epileptor Model

2020 ◽  
Vol 30 (11) ◽  
pp. 2050062
Author(s):  
João Angelo Ferres Brogin ◽  
Jean Faber ◽  
Douglas Domingues Bueno
Keyword(s):  
Nonlinear Dynamics ◽  
Mathematical Model ◽  
Exact Solution ◽  
Feedback Control ◽  
Epileptic Seizures ◽  
Neural Dynamics ◽  
Practical Application ◽  
Control Gain ◽  
The World ◽  
The Mathematical Model

Epilepsy affects about 70 million people in the world. Every year, approximately 2.4 million people are diagnosed with epilepsy, two-thirds of them will not know the etiology of their disease, and 1% of these individuals will decease as a consequence of it. Due to the inherent complexity of predicting and explaining it, the mathematical model Epileptor was recently developed to reproduce seizure-like events, also providing insights to improve the understanding of the neural dynamics in the interictal and ictal periods, although the physics behind each parameter and variable of the model is not fully established in the literature. This paper introduces an approach to design a feedback-based controller for suppressing epileptic seizures described by Epileptor. Our work establishes how the nonlinear dynamics of this disorder can be written in terms of a combination of linear sub-models employing an exact solution. Additionally, we show how a feedback control gain can be computed to suppress seizures, as well as how specific shapes applied as input stimuli for this purpose can be obtained. The practical application of the approach is discussed and the results show that the proposed technique is promising for developing controllers in this field.

Anticipation of epileptic seizures from standard EEG recordings

The Lancet ◽  
2001 ◽  
Vol 357 (9251) ◽  
pp. 183-188 ◽  
Author(s):  
Michel Le Van Quyen ◽  
Jacques Martinerie ◽  
Vincent Navarro ◽  
Paul Boon ◽  
Michel D'Havé ◽  
...  
Keyword(s):  
Epileptic Seizures ◽  
Eeg Recordings
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