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. 2018 Sep 12:2018:8463256.
doi: 10.1155/2018/8463256. eCollection 2018.

Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

Achmad Rizal  1 , Sugondo Hadiyoso  2
Affiliations

Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

Achmad Rizal et al. ScientificWorldJournal. .

Abstract

Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.

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Figures

Figure 1
Figure 1
(a) ictal EEG, (b) interictal EEG, (c) normal EEG.
Figure 2
Figure 2
Illustration of MSLD [11].
Figure 3
Figure 3
Hyperplane classifies data into two classes.
Figure 4
Figure 4
Illustration of finding the best hyperplane between two classes.
Figure 5
Figure 5
Illustration of 5-fold CV.
Figure 6
Figure 6
MSLD results of seizure EEG signal.
Figure 7
Figure 7
Sample entropy (r = 2.5) with MLSD for each distance d.
See this image and copyright information in PMC

References

    1. Goldberger A. L., Moody G. B., Costa M. D. Variability vs. Complexity. 2012. http://physionet.org/tutorials/cv/
    1. Costa M., Goldberger A. L., Peng C. Multiscale entropy analysis of biological signals. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics. 2005;71(2) doi: 10.1103/PhysRevE.71.021906.021906 - DOI - PubMed
    1. Uyulan C., Erguzel T. T. Analysis of Time - Frequency EEG Feature Extraction Methods for Mental Task Classification. International Journal of Computational Intelligence Systems. 2017;10(1):p. 1280. doi: 10.2991/ijcis.10.1.87. - DOI
    1. Liu Q., Chen Y.-F., Fan S.-Z., Abbod M. F., Shieh J.-S. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks. Computational and Mathematical Methods in Medicine. 2015;2015:16. doi: 10.1155/2015/232381.232381 - DOI - PMC - PubMed
    1. Mizuno T., Takahashi T., Cho R. Y., et al. Assessment of EEG dynamical complexity in Alzheimer's disease using multiscale entropy. Clinical Neurophysiology. 2010;121(9):1438–1446. doi: 10.1016/j.clinph.2010.03.025. - DOI - PMC - PubMed