scholarly journals An Approach to Seizure Onset Detection Using Fuzzy Logic Based on Seizure Evolution in Intracranial EEG

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Ahmed Rabbi ◽  
Reza Fazel-Rezai
Keyword(s):  
Fuzzy Logic ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg

scholarly journals An algorithm for seizure onset detection using intracranial EEG

2011 ◽  
Vol 22 ◽  
pp. S29-S35 ◽  
Author(s):  
Alaa Kharbouch ◽  
Ali Shoeb ◽  
John Guttag ◽  
Sydney S. Cash
Keyword(s):  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg

Automated seizure onset detection for accurate onset time determination in intracranial EEG

2008 ◽  
Vol 119 (12) ◽  
pp. 2687-2696 ◽  
Author(s):  
Alexander M. Chan ◽  
Felice T. Sun ◽  
Erem H. Boto ◽  
Brett M. Wingeier
Keyword(s):  
Onset Time ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
Time Determination

scholarly journals Dynamic Training of a Novelty Classifier Algorithm for Real-Time Early Seizure Onset Detection

2021 ◽  
Author(s):  
Daniel Ehrens ◽  
Mackenzie C. Cervenka ◽  
Gregory K. Bergey ◽  
Christophe C. Jouny
Keyword(s):  
Real Time ◽  
High Performance ◽  
Epileptiform Activity ◽  
Support Vector ◽  
List Type ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
Early Seizure ◽  
Epilepsy Monitoring

AbstractThe objective of this study was to develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels. This is done to evaluate the novelty of the current instance according to previous activity. Our algorithm was tested on intracranial EEG from human epilepsy patients that are admitted to the EMU for presurgical evaluation. In this study, we compared multiple configurations for using a one-class SVM to assess if there is significance over specific neural features or electrode locations. Our results show our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false-positive rate and robustness to different types of seizure-onset patterns as well as to the number of channels used. This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.HighlightsThis study proposes a dynamic training algorithm that efficiently detects sudden novel changes in intracranial electroencephalographic activity, creating a reliable seizure onset detection algorithm that does not need prior training.The algorithm described has the capability to be implemented in real-time, independently of the number of channels that are being analyzed.The presented detector shows high performance and reliability to be easily implemented in the Epilepsy Monitoring Unit to quickly alert clinical staff of seizure events.

scholarly journals A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed Fazle Rabbi ◽  
Reza Fazel-Rezai
Keyword(s):  
Fuzzy Rule ◽  
Epileptogenic Zone ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
Fuzzy Algorithms ◽  
Remote Zone ◽  
Threshold Procedure ◽  
Eeg Recordings ◽  
Logic System

We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.

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