Muhammad Yazid, Fahmi Fahmi, Erwin Sutanto, Wervyan Shalannanda, Ruhush Shoalihin, Gwo-Jiun Horng, Aripriharta
This paper proposed a simple but highly accurate feature extraction method for epilepsy detection from electroencephalogram (EEG) signals. Based on the combination of Discrete Wavelet Transform (DWT) and the newly proposed features Local Binary Pattern Transition Histogram (LBPTH) and Local Binary Pattern Mean Absolute Deviation (LBPMAD), our proposed feature extraction method can efficiently extract features from EEG signals for machine learning classification of epilepsy, achieving high classification accuracy with a feature size of only 18 for each signal. Tested on the publicly available University of Bonn Epilepsy EEG Dataset using a signal length of 4097 data points (23.61 seconds), the proposed method achieved larger than 99.6% accuracy results for Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classification of ictal (set E) against any non-ictal (set A, B, C, or D) or combinations of non-ictal (set A+B , set C+D , or set A+B+C+D) EEG signals, which is among the best of currently published works. Our method can maintain high classification accuracy even with short input signals, achieving more than 99.1% SVM classification accuracy when input signal length is reduced to 512 data points (2.95 seconds). The high accuracy, small feature size, ability to work with short input signals and low computing requirements made the proposed method suitable for mobile, low power, and low-cost wearable medical devices. © 2013 IEEE.
Biomedical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia; Department of Electronic Engineering, Faculty of Engineering, Universitas Sumatera Utara, Medan, 20155, Indonesia; Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Surabaya, 60115, Indonesia; Department of Electronic Engineering, School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40116, Indonesia; Faculty of Computer Science, University of Indonesia, Depok, 16424, Indonesia; Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, 71005, Taiwan; Department of Electrical Engineering, Universitas Negeri Malang, Malang, 65145, Indonesia
