Prediction of epileptic seizures from two-channel EEG

Salant, Y.; Gath, Isak; Henriksen, Olaf

doi:10.1007/bf02524422

Cited by 79 publications

(36 citation statements)

References 19 publications

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“…It is our plan to investigate further methods in which the DFA algorithm can be successfully employed. This includes the process of integrating KDD with the DFA methods and also considering the use of time domain analysis of EEG signal by statistical analysis and characteristics computation [13] with different frequencies [22], non-linear dynamics and chaos theory [8], and intelligent systems such as artificial neural network and other artificial-intelligence structures [6], [16].…”

Section: Discussionmentioning

confidence: 99%

Deterministic Finite Automata in the Detection of EEG Spikes and Seizures

Lewis

Shmueli

White

2010

Lecture Notes in Computer Science

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Abstract. This Paper presents a platform to mine epileptiform activity from Electroencephalograms (EEG) by combining the methodologies of Deterministic Finite Automata (DFA) and Knowledge Discovery in Data Mining (KDD) TV-Tree. Mining EEG patterns in human brain dynamics is complex yet necessary for identifying and predicting the transient events that occur before and during epileptic seizures. We believe that an intelligent data analysis of mining EEG Epileptic Spikes can be combined with statistical analysis, signal analysis or KDD to create systems that intelligently choose when to invoke one or more of the aforementioned arts and correctly predict when a person will have a seizure. Herein, we present a correlation platform for using DFA and Action Rules in predicting which interictal spikes within noise are predictors of the clinical onset of a seizure.

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Section: Discussionmentioning

confidence: 99%

Deterministic Finite Automata in the Detection of EEG Spikes and Seizures

Lewis

Shmueli

White

2010

Lecture Notes in Computer Science

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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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“…They used a linear approach to find seizure precursors [6]. Rogowski et al [7] and then Salant et al [8] used an autoregressive model to find changes prior seizure onsets. Iaesemidis et al [9] employed the Lyapunov exponent and an open window analysis and revealed a decrease in chaotic behavior of EEG signal before seizures.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System

Niknazar¹,

Maghooli²,

Nasrabadi³

2015

IJCA

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Epileptic seizures are generated by abnormal activity of neurons. EEG-based epileptic seizure prediction could be a key to improve life style of patients that suffer from drugresistance epilepsy. In this study, we propose a fuzzy logic system to predict epileptic seizures by using statistical behavior of local extrema (SBLE) features and a rule-based fuzzy system. Two approaches are considered to evaluate the proposed method. First approach is patient-dependent, which requires EEG data in preictal and interictal state. Second approach is leave one out (LOO) technique to evaluate generalizability of the method. Applied to the Freiburg EEG dataset, it was found that the method has good performance for most of the patients of this database. In the patientdependent approach, sensitivity of 84% with no false alarm and sensitivity of 94.15% with a false alarm rate of 0.1 were achieved. LOO evaluation approach obtained a sensitivity of 79.38% with a false alarm rate of 0.049. It is remarkable that for many of patients, the proposed method achieved sensitivity of 100% with no false alarm in both of evaluation approaches. This study showed that application of SBLE features as inputs of fuzzy logic system is a suitable way to track EEG changes leading to epileptic seizures.

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Prediction of epileptic seizures from two-channel EEG

Cited by 79 publications

References 19 publications

Deterministic Finite Automata in the Detection of EEG Spikes and Seizures

Deterministic Finite Automata in the Detection of EEG Spikes and Seizures

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

Epileptic Seizure Prediction using Statistical Behavior of Local Extrema and Fuzzy Logic System

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