Classification of epilepsy using computational intelligence techniques
Classification of epilepsy using computational intelligence techniques作者机构：Department of Informatics King's College London Strand London WC2R 2LS United Kingdom State Key Laboratory of Cognitive Neuroscience and Learning School of Brain and Cognitive Sciences Beijing Normal University No.19 XinJieKoWai St Hai Dian District Beijing 100875 PR China School of Electronic and Information Engineering Beijing Jiaotong University Beijing PR China
出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)
年 卷 期：2016年第1卷第2期
页 面：137-149页
学科分类：0710[理学-生物学] 07[理学] 071006[理学-神经生物学]
基 金：King's College London China Scholar Council National Natural Science Foundation of China Foreign Experts Scheme of China [GDW20151100010]
主 题：Absence seizure Discrete wavelet transform Epilepsy classification Feature extraction k-means clustering k-nearest neighbours Naive Bayes Neuralnetworks Support vector machines
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 su- pervised 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 （k- NN） 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 accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise.
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