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 The fence post depth electrode technique to control both brain tumors and epileptic seizures in patients with brain tumor-related epilepsy

2019 ◽  
Vol 10 ◽  
pp. 187 ◽  
Author(s):  
Yosuke Masuda ◽  
Ayataka Fujimoto ◽  
Mitsuyo Nishimura ◽  
Keishiro Sato ◽  
Hideo Enoki ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Seizure Onset ◽  
Tumor Removal ◽  
Depth Electrodes ◽  
Fence Post ◽  
Group 2 ◽  
The Brain ◽  
Group 1

Background: To control brain tumor-related epilepsy (BTRE), both epileptological and neuro-oncological approaches are required. We hypothesized that using depth electrodes (DEs) as fence post catheters, we could detect the area of epileptic seizure onset and achieve both brain tumor removal and epileptic seizure control. Methods: Between August 2009 and April 2018, we performed brain tumor removal for 27 patients with BTRE. Patients who underwent lesionectomy without DEs were classified into Group 1 (13 patients) and patients who underwent the fence post DE technique were classified into Group 2 (14 patients). Results: The patients were 15 women and 12 men (mean age, 28.1 years; median age 21 years; range, 5–68 years). The brain tumor was resected to a greater extent in Group 2 than Group 1 (P < 0.001). Shallower contacts showed more epileptogenicity than deeper contacts (P < 0.001). Group 2 showed better epilepsy surgical outcomes than Group 1 (P = 0.041). Conclusion: Using DEs as fence post catheters, we detected the area of epileptic seizure onset and controlled epileptic seizures. Simultaneously, we removed the brain tumor to a greater extent with fence post DEs than without.

scholarly journals Analysis of EEG signal of specific epileptic patient prior to its occurrence

SCITECH Nepal ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 16-23
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Amit Shah ◽  
Bhoj Raj Thapa
Keyword(s):  
Epileptic Seizure ◽  
Predictive Value ◽  
Epileptic Patient ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unique Pattern ◽  
Multi Resolution Analysis ◽  
The Individual

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper is focussed on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi-resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MJT­-BIH of one of the patient with 41 different cases is used. The result showed that a unique pattern is observed during the spectral analysis of the signal over different bands with positive predictive value of 100%, negative predictive value of 82.35% and the overall accuracy of 85.37%. This unique pattern, basically energy burst in two of the bands of the signal can be used as important feature for the early detection of the epileptic seizure. All the results have been simulated within the Matlab environment.

scholarly journals Implementing Neural Network and Multi resolution analysis in EEG signal for early detection of epilepsy

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Bhojraj Thapa
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Epileptic Seizure ◽  
Principal Component ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper focuses on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MIT-BIH of seven patients with different cases of epileptic seizure was used. The result with one of the patients showed presence of a unique pattern during the spectral analysis of the signal over different bands. Hence, based on the first patient, similar region is selected with the other patients and the multi-resolution analysis along with the principal component analysis (PCA) for the dimension reduction is carried out. Finally, these are treated with neural network to perform the classification of the signal either the epilepsy is occurring or not. The final results showed 100% accuracy with the detection with the neural network however it uses a large amount of data for analysis. Thus, the same was tested with dimension reduction using PCA which reduced the average accuracy to 89.51%. All the results have been simulated within the Matlab environment.

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