지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.9 No.3
- 발행연도
- 2014.5
- 수록면
- 1,060 - 1,071 (12page)
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초록· 키워드
오류제보하기
Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.
목차
Abstract
1. Introduction
2. Related Works
3. Proposed Work
4. Dataset Information
5. Experimental Results and Discussion
6. Conclusion
References
참고문헌 (37)
참고문헌 신청
1. [학술지(정기간행물)] - Frederic / 2006 / Supervised Neuronal Approaches for EEG Signal Classification : Experimental Studies / Artificial Intelligence and Soft Computing. ACTA Press 29 : 1 ~ 6
2. [학술지(정기간행물)] - George Tsolis / 2011 / Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics / International Journal of Signal Processing, Image Processing and Pattern Recognition 4 (2) : 91 ~ 106
3. [학술지(정기간행물)] - NihalFatma Guler / 2005 / Recurrent Neural Networks Employing Lyapunov Exponents for EEG Signals Classification / Expert Systems with Applications 29 (3) : 506 ~ 514
4. [학술지(정기간행물)] - Claude Robert / 2002 / Electroencephalogram Processing Using Neural Networks / Clinical Neurophysiology 113 (5) : 694 ~ 701
5. [학술지(정기간행물)] - James, Christopher J. / 2004 / Independent Component Analysis for Biomedical Signals / Physiological measurement 26 (1) : 633 ~ 644
2. [학술지(정기간행물)] - George Tsolis / 2011 / Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics / International Journal of Signal Processing, Image Processing and Pattern Recognition 4 (2) : 91 ~ 106
3. [학술지(정기간행물)] - NihalFatma Guler / 2005 / Recurrent Neural Networks Employing Lyapunov Exponents for EEG Signals Classification / Expert Systems with Applications 29 (3) : 506 ~ 514
4. [학술지(정기간행물)] - Claude Robert / 2002 / Electroencephalogram Processing Using Neural Networks / Clinical Neurophysiology 113 (5) : 694 ~ 701
5. [학술지(정기간행물)] - James, Christopher J. / 2004 / Independent Component Analysis for Biomedical Signals / Physiological measurement 26 (1) : 633 ~ 644
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UCI(KEPA) : I410-ECN-0101-2015-500-001462831