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Soft Computing

, Volume 23, Issue 1, pp 227–239 | Cite as

Epileptic seizures detection in EEGs blending frequency domain with information gain technique

  • Hadi Ratham Al GhayabEmail author
  • Yan Li
  • Siuly Siuly
  • Shahab Abdulla
Methodologies and Application
  • 69 Downloads

Abstract

This paper proposes a new algorithm which combines the information in frequency domain with the Information Gain (InfoGain) technique for the detection of epileptic seizures from electroencephalogram (EEG) data. The proposed method consists of four main steps. Firstly, in order to investigate which method is most suitable to decompose the EEG signals into frequency bands, we implement separately a fast Fourier transform (FFT) or discrete wavelet transform (DWT). Secondly, each band is partitioned into k windows and a set of statistical features are extracted from each window. Thirdly, the InfoGain is used to rank the extracted features and the most important ones are selected. Lastly, these features are forwarded to a least square support vector machine (LS-SVM) classifier to classify the EEG. This scheme is implemented and tested on a benchmark EEG database and also compared with other existing methods, based on some performance evaluation measures. The experimental results show that the proposed FFT combined with InfoGain method can generate better performance than the DWT method. This method achieves 100% accuracy for five different pairs: healthy people with eyes open (z) versus epileptic patients with activity seizures (s); healthy people with eyes closed (o) versus s; epileptic patients with free seizures (n) versus s; patients with free seizures epileptic (f) versus s; and z versus o. The accuracies obtained for two other pairs, (o vs. n) and (z vs. f), are 95.62 and 88.32%, respectively. These two pairs have more similarities with each other, leading to a lower level of accuracy. The proposed approach outperforms six other reported methods and achieves an 11.9% improvement. Finally, it can be concluded that the proposed FFT combined with InfoGain method has the capacity to detect epileptic seizures in EEG most effectively.

Keywords

Electroencephalogram Epileptic seizures Frequency domain Information gain technique Least square support vector machine 

Notes

Acknowledgements

The first author acknowledges the Iraqi government (Ministry of Higher Education and scientific research) for providing PhD scholarship.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Acharya UR, Vidya KS, Ghista DN et al (2015) Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowl Based Syst 81:56–64CrossRefGoogle Scholar
  2. Al Ghayab HR, Li Y, Abdulla S et al (2016) Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Inform 3(2):85–91CrossRefGoogle Scholar
  3. Al Ghayab HR, Li Y, Siuly S et al (2017) Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction. In: International conference on health information science. Springer, Cham, pp 45–55Google Scholar
  4. Al Ghayab HR, Li Y, Siuly S et al (2018) Epileptic EEG signal classification using optimum allocation based power spectral density estimation. IET Signal Process 12(6):738–747CrossRefGoogle Scholar
  5. Amin HU, Malik AS, Ahmad RF et al (2015) Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australas Phys Eng Sci Med 38:139–149CrossRefGoogle Scholar
  6. Chua KC, Chandran V, Acharya UR et al (2011) Application of higher order spectra to identify epileptic EEG. J Med Syst 35:1563–1571CrossRefGoogle Scholar
  7. Deng JD, Simmermacher C, Cranefield S (2008) A study on feature analysis for musical instrument classification. IEEE Trans Syst Man Cybern B Cybern 38:429–438CrossRefGoogle Scholar
  8. Gajic D, Djurovic Z, Di Gennaro S et al (2014) Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed Eng Appl Basis Commun 26:1450021CrossRefGoogle Scholar
  9. Gajic D, Djurovic Z, Gligorijevic J et al (2015) Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis. Front Comput Neurosci 9:38CrossRefGoogle Scholar
  10. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Elsevier, AmsterdamzbMATHGoogle Scholar
  11. Heckbert P (1995) Fourier transforms and the fast Fourier transform (FFT) algorithm. Comput Graph 2:15–463Google Scholar
  12. Kaya Y, Uyar M, Tekin R et al (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl Math Comput 243:209–219MathSciNetzbMATHGoogle Scholar
  13. Koprinska I (2010) Feature selection for brain-computer interfaces. In: Theeramunkong T et al (eds) New Frontiers in Applied Data Mining. PAKDD 2009. Lecture Notes in Computer Science, vol 5669. Springer, Berlin, Heidelberg, pp 106–117CrossRefGoogle Scholar
  14. LS-SVMlab toolbox (version 1.8) (2011) http://www.esat.kuleuven.ac.be/sista/lssvmlab/. Accessed Nov 2016
  15. Mcgrogan N (1999) Neural network detection of epileptic seizures in the electroencephalogram. Dissertation, University of OxfordGoogle Scholar
  16. Nicolaou N, Georgiou J (2012) Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39:202–209CrossRefGoogle Scholar
  17. Rao T, Vishwanath DD (2014) Detecting sleep disorders based on EEG signals by using discrete wavelet transform. In: 2014 International conference on green computing communication and electrical engineering (ICGCCEE). IEEE, pp 1–5Google Scholar
  18. Samiee K, Kovacs P, Gabbouj M (2015) Epileptic seizure classification of eeg time-series using rational discrete short-time fourier transform. IEEE Trans Biomed Eng 62:541–552CrossRefGoogle Scholar
  19. Shen C-P, Chen C-C, Hsieh S-L et al (2013) High-performance seizure detection system using a wavelet-approximate entropy-fSVM cascade with clinical validation. Clin EEG Neurosci 44:247–256CrossRefGoogle Scholar
  20. Siuly S, Li Y (2015) Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification. Comput Methods Program Biomed 119(1):29–42CrossRefGoogle Scholar
  21. Siuly S, Kabir E, Wang H et al (2015) Exploring sampling in the detection of multicategory EEG signals. Comput Math Methods Med 2015:576437CrossRefGoogle Scholar
  22. Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRefGoogle Scholar
  23. Swami P, Gandhi TK, Panigrahi BK et al (2016) A comparative account of modelling seizure detection system using wavelet techniques. Int J Syst Sci Oper Logist 4:41–52Google Scholar
  24. Tzimourta KD, Tzallas AT, Giannakeas N, Astrakas LG, Tsalikakis DG, Tsipouras MG (2018) Epileptic seizures classification based on long-term EEG signal wavelet analysis. In: Maglaveras N, Chouvarda I, de Carvalho P (eds) Precision Medicine Powered by pHealth and Connected Health. IFMBE Proceedings, vol 66. Springer, Singapore, pp 165–169CrossRefGoogle Scholar
  25. Wali MK, Murugappan M, Ahmmad B (2013) Wavelet packet transform based driver distraction level classification using EEG. Math Probl Eng 2013:10CrossRefGoogle Scholar
  26. Wang S, Zhu G, Li Y et al (2014) Analysis of epileptic EEG signals with simple random sampling J48 algorithm. Int J Biosci Biochem Bioinform 4:78Google Scholar
  27. World Health Organization (WHO) (2011) Report: WHO. http://www.who.int/mediacentre/factssheets/fs999/en/index.html. Accessed Dec 2015
  28. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: ICML, pp 412–420Google Scholar
  29. Yuan Q, Zhou W, Li S et al (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96:29–38CrossRefGoogle Scholar
  30. Zhu G, Li Y, Wen PP et al (2013) Unsupervised classification of epileptic EEG signals with multi scale K-means algorithm. In: Brain and health informatics. Springer, Berlin, p 158–167Google Scholar
  31. Zonst AE (1995) Understanding the FFT: a tutorial on the algorithm & software for laymen, students, technicians & working engineers. Citrus Press, FloridaGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hadi Ratham Al Ghayab
    • 1
    • 2
    Email author
  • Yan Li
    • 1
  • Siuly Siuly
    • 3
  • Shahab Abdulla
    • 4
  1. 1.Faculty of Health, Engineering and SciencesUniversity of Southern QueenslandToowoombaAustralia
  2. 2.Collage of Computer Sciences and MathematicsUniversity of Thi-QarNasiriyahIraq
  3. 3.Centre for Applied Informatics, College of Engineering and ScienceVictoria UniversityMelbourneAustralia
  4. 4.Open Access CollegeUniversity of Southern QueenslandToowoombaAustralia

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