Research Area:  Machine Learning

Abstract:

This paper presents a pattern recognition model using multiple features and the kernel extreme learning machine (ELM), improving the accuracy of automatic epilepsy diagnosis. After simple preprocessing, temporal- and wavelet-based features are extracted from epileptic EEG signals. A combined kernel-function-based ELM approach is then proposed for feature classification. To further reduce the computation, Cholesky decomposition is introduced during the process of calculating the output weights. The experimental results show that the proposed method can achieve satisfactory accuracy with less computation time.

Keywords:  
Epileptic Seizure Detection
Kernel Extreme Learning Machine
pattern recognition
epilepsy diagnosis
Cholesky decomposition
Machine Learning
Deep Learning

Author(s) Name:  Liu, Qia; Zhao, Xiaoguang; Hou, Zengguang ; Liu, Hongguang

Publisher name:  IOS Press

DOI:  10.3233/THC-171343

Volume Information:  vol. 25, no. S1, pp. 399-409, 2017

Paper Link:   https://content.iospress.com/articles/technology-and-health-care/thc1343