scholarly journals Automatic Seizure Onset Detection in Long Term Pediatric EEG Signals

2016 ◽  
Vol 9 (4) ◽  
Author(s):  
Saeed MT
Keyword(s):  
Onset Detection ◽  
Eeg Signals ◽  
Seizure Onset

scholarly journals One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG

2021 ◽  
Author(s):  
Xiaoshuang Wang ◽  
Xiulin Wang ◽  
Wenya Liu ◽  
Zheng Chang ◽  
Tommi Karkkainen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
One Dimensional

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.

scholarly journals A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

2016 ◽  
Vol 705 ◽  
pp. 012032 ◽  
Author(s):  
Antonio Quintero-Rincón ◽  
Marcelo Pereyra ◽  
Carlos D’Giano ◽  
Hadj Batatia ◽  
Marcelo Risk
Keyword(s):  
Onset Detection ◽  
Eeg Signals ◽  
Seizure Onset ◽  
Spread Estimation

Long-term seizure outcomes after pediatric temporal lobectomy: does brain MRI lesion matter?

2019 ◽  
Vol 24 (2) ◽  
pp. 200-208
Author(s):  
Ravindra Arya ◽  
Francesco T. Mangano ◽  
Paul S. Horn ◽  
Sabrina K. Kaul ◽  
Serena K. Kaul ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Hippocampal Sclerosis ◽  
Brain Mri ◽  
Temporal Lobectomy ◽  
Seizure Freedom ◽  
Seizure Onset

OBJECTIVEThere is emerging data that adults with temporal lobe epilepsy (TLE) without a discrete lesion on brain MRI have surgical outcomes comparable to those with hippocampal sclerosis (HS). However, pediatric TLE is different from its adult counterpart. In this study, the authors investigated if the presence of a potentially epileptogenic lesion on presurgical brain MRI influences the long-term seizure outcomes after pediatric temporal lobectomy.METHODSChildren who underwent temporal lobectomy between 2007 and 2015 and had at least 1 year of seizure outcomes data were identified. These were classified into lesional and MRI-negative groups based on whether an epilepsy-protocol brain MRI showed a lesion sufficiently specific to guide surgical decisions. These patients were also categorized into pure TLE and temporal plus epilepsies based on the neurophysiological localization of the seizure-onset zone. Seizure outcomes at each follow-up visit were incorporated into a repeated-measures generalized linear mixed model (GLMM) with MRI status as a grouping variable. Clinical variables were incorporated into GLMM as covariates.RESULTSOne hundred nine patients (44 females) were included, aged 5 to 21 years, and were classified as lesional (73%), MRI negative (27%), pure TLE (56%), and temporal plus (44%). After a mean follow-up of 3.2 years (range 1.2–8.8 years), 66% of the patients were seizure free for ≥ 1 year at last follow-up. GLMM analysis revealed that lesional patients were more likely to be seizure free over the long term compared to MRI-negative patients for the overall cohort (OR 2.58, p < 0.0001) and for temporal plus epilepsies (OR 1.85, p = 0.0052). The effect of MRI lesion was not significant for pure TLE (OR 2.64, p = 0.0635). Concordance of ictal electroencephalography (OR 3.46, p < 0.0001), magnetoencephalography (OR 4.26, p < 0.0001), and later age of seizure onset (OR 1.05, p = 0.0091) were associated with a higher likelihood of seizure freedom. The most common histological findings included cortical dysplasia types 1B and 2A, HS (40% with dual pathology), and tuberous sclerosis.CONCLUSIONSA lesion on presurgical brain MRI is an important determinant of long-term seizure freedom after pediatric temporal lobectomy. Pediatric TLE is heterogeneous regarding etiologies and organization of seizure-onset zones with many patients qualifying for temporal plus nosology. The presence of an MRI lesion determined seizure outcomes in patients with temporal plus epilepsies. However, pure TLE had comparable surgical seizure outcomes for lesional and MRI-negative groups.

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