On the prediction of epileptic seizures

Rogowski, Zeev; Gath, Isak; Bental, E

doi:10.1007/bf00335153

Cited by 166 publications

(63 citation statements)

References 14 publications

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“…It was reported that seizures could be predicted several seconds, minutes or hours before occurrence, depending on the technique. For instance, autoregressive modelling [54,55] found pre-ictal changes in the modeled parameters up to six seconds before seizure onset.…”

Section: A21 Analytical Methodsmentioning

confidence: 99%

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Dunn¹,

Abbott²,

Masterton³

et al. 2011

ANZIAMJ

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The Brain Research Institute (bri) uses various types of indirect measurements, including eeg and fmri, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the bri asked the misg 2010 participants to examine how the available eeg and fmri data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was

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Section: A21 Analytical Methodsmentioning

confidence: 99%

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Dunn¹,

Abbott²,

Masterton³

et al. 2011

ANZIAMJ

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show abstract

“…However, the limitations of the detection time are the major challenge. Wireless scalp EEG based early prediction, warning and detection of epilepsy seizure systems were proposed and mentioned by [7] [8]. According to and WHO 2016 [9], a detection system should be:…”

Section: Fig 1: the Global Epilepsy Rate Research University Of Oxfomentioning

confidence: 99%

Early Prediction of Epilepsy Seizures System based on Artificial Immune BCI System

Zaghloul¹,

Elsayed²,

Bayoumi³

2017

IJCA

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Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain Computer/Machine Interface (BCI/BMI). Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80% of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor impaired patients, especially due to the fact that many medical personnel are not qualified for EEG signal analysis. Therefore, a portable automated detection and monitoring solution is in high demand. Thus, in this study a system of a wireless wearable adaptive for early prediction of epilepsy seizures is proposed, works via minimally invasive wireless technology paired with an external control device (e.g., a doctors' smartphone), with a higher than standard accuracy (71%) and prediction time (14.56 sec). This novel architecture has not only opened new opportunities for daily usable BCI implementations, but they can also save a life by helping to prevent a seizure's fatal consequences. General TermsBrain Machine Interface, Artificial Immune, Signal Processing.

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“…The EEG has since become one of most useful tools for studying the cognitive processes and the physiology/pathology of the brain [8,9], especially the processes involved in epileptic seizures [10,11]. Currently, these methods mainly include traditional linear methods such as Fourier transforms and spectral analysis [12] and nonlinear methods such as Lyapunov exponents [13], correlation dimension [14] and similarity [15,16]. In particular, a series of entropy-based approaches has been widely used since they can quantify the complexity (regularity) of an EEG signal [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

Yan

Liu

et al. 2014

Entropy

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Abstract:In this paper, we propose to use permutation entropy to explore whether the changes in electroencephalogram (EEG) data can effectively distinguish different phases in human absence epilepsy, i.e., the seizure-free, the pre-seizure and seizure phases. Permutation entropy is applied to analyze the EEG data from these three phases, each containing 100 19-channel EEG epochs of 2 s duration. The experimental results show the mean value of PE gradually decreases from the seizure-free to the seizure phase and provides evidence that these three different seizure phases in absence epilepsy can be effectively distinguished. Furthermore, our results strengthen the view that most frontal electrodes carry useful information and patterns that can help discriminate among different absence seizure phases.

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On the prediction of epileptic seizures

Cited by 166 publications

References 14 publications

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Early Prediction of Epilepsy Seizures System based on Artificial Immune BCI System

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

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