Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Amin Zare; Reza Boostani; Mansour Zolghadr Jahromi
Volume 4, Issue 3 , June 2010, , Pages 195-208
Abstract
There is a growing interest to improve seizure prediction by online analyzing of electroencephalogram (EEG) signals in epileptic patients. Seizure attack is occurred infrequently and unpredictably; hence, automatic detection of seizure during long-term is highly recommended. In this paper a novel Feature ...
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There is a growing interest to improve seizure prediction by online analyzing of electroencephalogram (EEG) signals in epileptic patients. Seizure attack is occurred infrequently and unpredictably; hence, automatic detection of seizure during long-term is highly recommended. In this paper a novel Feature Reduction method namely AIS-RCA which adopted from the immunity system is proposed to improve the seizure detection rate. The automatic seizure detection can be performed in two successive stages: 1) The feature extraction/selection stage from EEG signals and 2) classifying the feature vectors by an efficient classifier. In this study, first, pseudo-Wigner-Ville distribution was applied to each window of the EEG signals and then the extracted features were transformed by AIS-RCA transform to represent the features in a more separable space. The AIS-RCA transformation matrix is estimated by using chunklets (a chunklet is defined as a subset of points that are known to be same). AIS-RCA using the proposed Artificial Immune System algorithm named Adaptive Distance-AIRS to discover the chunklets in the data space. Finally KNN classifier was applied to the transformed features to classify the seizure and non-seizure windows. The experimental results show that the proposed method yields epileptic detection accuracy rate up to 99.9% which is better than the results achieved by other types of features such as FFT, Wavelet transform, entropy and chaotic measures.
