Epileptic seizures detection in EEGs blending frequency domain with information gain technique
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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.
KeywordsElectroencephalogram Epileptic seizures Frequency domain Information gain technique Least square support vector machine
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.
- 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
- Heckbert P (1995) Fourier transforms and the fast Fourier transform (FFT) algorithm. Comput Graph 2:15–463Google Scholar
- LS-SVMlab toolbox (version 1.8) (2011) http://www.esat.kuleuven.ac.be/sista/lssvmlab/. Accessed Nov 2016
- Mcgrogan N (1999) Neural network detection of epileptic seizures in the electroencephalogram. Dissertation, University of OxfordGoogle Scholar
- 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
- 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
- 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
- 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
- World Health Organization (WHO) (2011) Report: WHO. http://www.who.int/mediacentre/factssheets/fs999/en/index.html. Accessed Dec 2015
- Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: ICML, pp 412–420Google Scholar
- 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
- Zonst AE (1995) Understanding the FFT: a tutorial on the algorithm & software for laymen, students, technicians & working engineers. Citrus Press, FloridaGoogle Scholar