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.

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

scholarly journals Delta rhythm in wakefulness: evidence from intracranial recordings in human beings

2015 ◽  
Vol 114 (2) ◽  
pp. 1248-1254 ◽  
Author(s):  
Robert N. S. Sachdev ◽  
Nicolas Gaspard ◽  
Jason L. Gerrard ◽  
Lawrence J. Hirsch ◽  
Dennis D. Spencer ◽  
...  
Keyword(s):  
High Frequency ◽  
Nonhuman Primates ◽  
Human Beings ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
Quiet Wakefulness ◽  
Delta Rhythm ◽  
Eeg Recordings ◽  
Low Amplitude ◽  
Intracranial Recordings

A widely accepted view is that wakefulness is a state in which the entire cortical mantle is persistently activated, and therefore desynchronized. Consequently, the EEG is dominated by low-amplitude, high-frequency fluctuations. This view is currently under revision because the 1–4 Hz delta rhythm is often evident during “quiet” wakefulness in rodents and nonhuman primates. Here we used intracranial EEG recordings to assess the occurrence of delta rhythm in 18 awake human beings. Our recordings reveal rhythmic delta during wakefulness at 10% of all recording sites. Delta rhythm could be observed in a single cortical lobe or in multiple lobes. Sites with high delta could flip between high and low delta power or could be in a persistently high delta state. Finally, these sites were rarely identified as the sites of seizure onset. Thus rhythmic delta can dominate the background operation and activity of some neocortical circuits in awake human beings.

scholarly journals Contribution of ictal source imaging for localizing seizure onset zone in patients with focal epilepsy patients

Neurology ◽  
2020 ◽  
pp. 10.1212/WNL.0000000000011109
Author(s):  
Shuai Ye ◽  
Lin Yang ◽  
Yunfeng Lu ◽  
Michal T. Kucewicz ◽  
Benjamin Brinkmann ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Focal Epilepsy ◽  
High Density ◽  
Concordance Rate ◽  
Added Value ◽  
Seizure Onset ◽  
Source Imaging ◽  
Intracranial Eeg ◽  
Seizure Onset Zone ◽  
Eeg Recordings

ObjectiveTo determine whether seizure onset zone can be accurately localized prior to surgical planning in focal epilepsy patients, we performed non-invasive EEG recordings and source localization analyses on 39 patients.MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison.ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients.ConclusionNoninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.

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 Detection of Epileptic Seizure Event and Onset Using EEG

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Nabeel Ahammad ◽  
Thasneem Fathima ◽  
Paul Joseph
Keyword(s):  
Epileptic Seizure ◽  
Wavelet Decomposition ◽  
Epileptic Seizures ◽  
Epileptogenic Zone ◽  
Onset Detection ◽  
Eeg Signals ◽  
Seizure Onset ◽  
Absolute Deviation ◽  
Linear Classifier ◽  
Seizure Event

This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. Important features such as energy, entropy, standard deviation, maximum, minimum, and mean at different subbands were computed and classification was done using linear classifier. The performance of classifier was determined in terms of specificity, sensitivity, and accuracy. The overall accuracy was 84.2%. In the case of seizure onset detection, the database used is CHB-MIT scalp EEG database. Along with wavelet based features, interquartile range (IQR) and mean absolute deviation (MAD) without wavelet decomposition were extracted. Latency was used to study the performance of seizure onset detection. Classifier gave a sensitivity of 98.5% with an average latency of 1.76 seconds.

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