SavedAdd your journal

Journals / Turkish Journal of Electrical Engineering and Computer Science / 2013 / Cilt: 21 - Sayı: Sup.1

Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

Baha ŞEN
Musa PEKER
Pages
2092–2109
Publication Date
2012-12-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms.
📊
Key Findings & Data:This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals.
💡
Conclusion & Impact:The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:EEG signals · classification algorithms · FCBF selection algorithm

Abstract

This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms. In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

Özet

This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms. In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

🔖

Cite This Article

ŞEN, B., PEKER, M. (2012). Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 21 - Sayı: Sup.1, 2092–2109. https://ajindex.com/articles/188610

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. S. Aydın, “Determination of autoregressive model orders for seizure detection”, Turkish Journal of Electrical Engineering & Computer Science, Vol. 18, pp. 23–30, 2010.
  2. World Health Organization, Epilepsy, Geneva, WHO, at http://www.who.int/mediacentre/factsheets/fs999/en/index.html, 20 R.B. Artan, E. Yazgan, “Epileptic seizure detection from SEEG data by using higher order statistics and spectra”, ˙IT ¨ U Dergisi, Vol. 7, pp. 102–111, 2008.
  3. T. Fathima, M. Bedeeuzzaman, O. Farooq, U.K. Yusuf, “Wavelet based features for epileptic seizure detection”, MES Journal of Technology and Management, pp. 108–112, 2010.
  4. M. Albayrak, E. K¨ okl¨ ukaya, “The detection of an epileptiform activity on EEG signals by using data mining process”, e-Journal of New World Sciences Academy, 4(1), article number 2A0001, 2009.
  5. C.T. Yuen, W.S. San, M. Rizoni, T.C. Seong, “Classification of human emotions from EEG signals using statistical features and neural network”, International Journal of Integrated Engineering, Vol. 1, pp. 71–79, 2009. A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model”, Expert Systems with Applications, Vol. 32, pp. 1084–1093, 2007.
  6. H.R. Mohseni, A. Maghsoudi, M.B. Shamsollahi, “Seizure detection in EEG signals: A comparison of different approaches”, Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6724–6727, 2006. M. D’Alessandro, G. Vachtsevanos, A. Hinson, R. Esteller, J. Echauz, B. Litt, “A genetic approach to selecting the optimal feature for epileptic seizure prediction”, Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2, pp. 1703–1706, 2001.
  7. N. Kannathal, M. Choo, U. Acharya, P. Sadasivan, “Entropies for detection of epilepsy in EEG”, Computer Methods and Programs in Biomedicine, Vol. 80, pp. 187–94, 2005.
  8. V. Srinivasan, C. Eswaran, N. Sriraam, “Artificial neural network based epileptic detection using time domain and frequency domain features”, Journal of Medical Systems, Vol. 29, pp. 647–660, 2005.
  9. F.S. Bao, D.Y. Lie, Y. Zhang, “A new approach to automated epileptic diagnosis using EEG and probabilistic neural network”, Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence, Vol. 2, pp. 482–486, 2008.
  10. L. Guo, D. Rivero, J.A. Seoane, A. Pazos, “Classification of EEG signals using relative wavelet energy and artificial neural ne