Classification of epilepsy using computational intelligence techniques

TY - JOUR

T1 - Classification of epilepsy using computational intelligence techniques

AU - Qazi, Khurram I.

AU - Lam, H.K.

AU - Xiao, Bo

AU - Ouyang, Gaoxiang

AU - Yin, Xunhe

PY - 2016/10/13

Y1 - 2016/10/13

N2 - 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 (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.

AB - 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 (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.

KW - Absence seizure

KW - Discrete wavelet transform

KW - Epilepsy classification

KW - Feature extraction

KW - k-means clustering

KW - k-nearest neighbours

KW - Naive Bayes

KW - Neural networks

KW - Support vector machines

U2 - 10.1016/j.trit.2016.08.001

DO - 10.1016/j.trit.2016.08.001

M3 - Article

SN - 2468-2322

JO - CAAI Transactions on Intelligence Technology

JF - CAAI Transactions on Intelligence Technology

ER -