A unified multi-level spectral–temporal feature learning framework for patient-specific seizure onset detection in EEG signals

2020 ◽  
Vol 205 ◽  
pp. 106152 ◽  
Author(s):  
Fang-Gui Tang ◽  
Yu Liu ◽  
Yang Li ◽  
Zi-Wen Peng
Keyword(s):  
Feature Learning ◽  
Patient Specific ◽  
Onset Detection ◽  
Eeg Signals ◽  
Seizure Onset ◽  
Learning Framework ◽  
Multi Level ◽  
Temporal Feature

scholarly journals Patient-specific seizure onset detection

2005 ◽  
Author(s):  
A. Shoeb ◽  
H. Edwards ◽  
J. Connolly ◽  
B. Bourgeois ◽  
T. Treves ◽  
...  
Keyword(s):  
Patient Specific ◽  
Onset Detection ◽  
Seizure Onset

scholarly journals Patient-specific seizure onset detection

2004 ◽  
Vol 5 (4) ◽  
pp. 483-498 ◽  
Author(s):  
Ali Shoeb ◽  
Herman Edwards ◽  
Jack Connolly ◽  
Blaise Bourgeois ◽  
S. Ted Treves ◽  
...  
Keyword(s):  
Patient Specific ◽  
Onset Detection ◽  
Seizure Onset

An Automatic Patient-Specific Seizure Onset Detection Method Using Intracranial Electroencephalography

2014 ◽  
Vol 18 (2) ◽  
pp. 79-84 ◽  
Author(s):  
Yu-xin Zheng ◽  
Jun-ming Zhu ◽  
Yu Qi ◽  
Xiao-xiang Zheng ◽  
Jian-min Zhang
Keyword(s):  
Detection Method ◽  
Patient Specific ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Electroencephalography

Patient-Specific Seizure Onset Detection Via Neural Synchrony

2014 ◽  
Author(s):  
Marwa Qaraqe ◽  
Muhammad Ismail ◽  
Erchin Serpedin
Keyword(s):  
Patient Specific ◽  
Neural Synchrony ◽  
Onset Detection ◽  
Seizure Onset

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
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