Research Area: Machine Learning
Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic electroencephalography (EEG) signal analysis as a powerful time-frequency tool. But some important problems have not yet been benefitted from DWT, including epileptic focus localization, a key task in epilepsy diagnosis and treatment. Additionally, the parameters and settings for DWT are chosen empirically or arbitrarily in previous work. In this work, we propose a framework to use DWT and support vector machine (SVM) for epileptic focus localization problem based on EEG. To provide a guideline in selecting the best settings for DWT, we decompose the EEG segments in seven commonly used wavelet families to their maximum theoretical levels. The wavelet and its level of decomposition providing the highest accuracy in each wavelet family are then used in a grid search for obtaining the optimal frequency bands and wavelet coefficient features. Our approach achieves promising performance on two widely-recognized intrancranial EEG datasets that are also seizure-free, with an accuracy of 83.07% on the Bern-Barcelona dataset and an accuracy of 88.00% on the UBonn dataset. Compared with existing DWT-based approaches in epileptic EEG analysis, the proposed approach leads to more accurate and robust results. A guideline for DWT parameter setting is provided at the end of the paper.
Keywords:
Discrete wavelet transform (DWT)
electroencephalography (EEG)
epileptic focus localization
time-frequency analysis
Machine Learning
Deep Learning
Author(s) Name: Duo Chen; Suiren Wan; Forrest Sheng Bao
Journal name: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TNSRE.2016.2604393
Volume Information: ( Volume: 25, Issue: 5, May 2017)
