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. 2016 Jun 1;6(2):81-94.
eCollection 2016 Jun.

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

Kh Rezaee  1 , E Azizi  2 , J Haddadnia  1
Affiliations

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

Kh Rezaee et al. J Biomed Phys Eng. .

Abstract

Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer's and stroke, it is the third widespread nervous disorder.

Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children's Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity.

Method: In this algorithm, L-sec epochs of signals are displayed in form of a third-order tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features.

Results: The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours.

Conclusion: Today, the lack of an automated system to detect or predict the seizure onset is strongly felt.

Keywords: EEG Signals; Epileptic Seizure; General Tensor Discriminant Analysis (GTDA); K-NN; Wavelet Transform.

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Figures

Figure 1
Figure 1
An Example of Seizure in EEG Signals of Patient A
Figure 2
Figure 2
An Example of Seizure in EEG Signals of Patient B
Figure 3
Figure 3
Implementation of Proposed Algorithm
Figure 4
Figure 4
How Spectral and Spatial Features are Extracted in Proposed Algorithm
Figure 5
Figure 5
Percentage of Detected Seizures within a Specified Latency
Figure 6
Figure 6
Percentage of Detected Seizures within a Specified Latency
Figure 7
Figure 7
Example of an EEG Seizure in Patient No. 15. Seizure Began in the Second 272 with a Theta-Band Rhythm, which is most Prominently Observable on Channels T7-P7
Figure 8
Figure 8
Another Example of EEG Seizures of Patient No. 15. These Seizures Began in the Second 876 with a Training of Pulses on Channel P7-O1.
Figure 9
Figure 9
Sensitivity of Patient-Specific Seizure Detector. Overall, 98% of 163 Seizures were Revealed.
Figure 10
Figure 10
Sensitivity of Patient-Specific Seizure Detector. Overall, 98% of 163 Seizures were Revealed.
See this image and copyright information in PMC

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