scholarly journals Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate

2012 ◽  
Vol 59 (2) ◽  
pp. 579-585 ◽  
Author(s):  
I. Conradsen ◽  
S. Beniczky ◽  
K. Hoppe ◽  
P. Wolf ◽  
H. B. D. Sorensen
Keyword(s):  
Epileptic Seizure ◽  
Onset Detection ◽  
Seizure Onset ◽  
Zero Crossing ◽  
Automated Algorithm ◽  
Zero Crossing Rate

Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection

2010 ◽  
Vol 7 (4) ◽  
pp. 046007 ◽  
Author(s):  
G E Polychronaki ◽  
P Y Ktonas ◽  
S Gatzonis ◽  
A Siatouni ◽  
P A Asvestas ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Epileptic Seizure ◽  
Onset Detection ◽  
Seizure Onset ◽  
Dimension Estimation ◽  
Estimation Algorithms

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.

Epileptic seizure onset detection based on EEG and ECG data fusion

2016 ◽  
Vol 58 ◽  
pp. 48-60 ◽  
Author(s):  
Marwa Qaraqe ◽  
Muhammad Ismail ◽  
Erchin Serpedin ◽  
Haneef Zulfi
Keyword(s):  
Data Fusion ◽  
Epileptic Seizure ◽  
Onset Detection ◽  
Seizure Onset ◽  
Ecg Data
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