智能技术学报

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稿件标题: Classification of epilepsy using computational intelligence techniques
稿件作者: Khurram I. Qazi, H.K. Lam, Bo Xiao, Gaoxiang Ouyang, Xunhe Yin
关键字词: Absence seizure; Discrete wavelet transform; Epilepsy classification; Feature extraction; k-means clustering; k-nearest neighbours; Naive Bayes; Neural networks; Support vector machines
文章摘要: This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network (ANN) and support vector machines (SVMs) combined with supervised learning algorithms, and k-means clustering (k-MC) combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One (OvO), One Vs All (OvA) and Binary Decision Tree (BDT), are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour (kNN) and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accurac
收录刊物: 2016年1卷2期
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