Journal
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
Volume 1, Issue 2, Pages 137-+Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1016/j.trit.2016.08.001
Keywords
Absence seizure; Discrete wavelet transform; Epilepsy classification; Feature extraction; k-means clustering; k-nearest neighbours; Naive Bayes; Neural networks; Support vector machines
Categories
Funding
- King's College London
- China Scholar Council
- National Natural Science Foundation of China [61172022]
- Foreign Experts Scheme of China [GDW20151100010]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available