Computer-aided diagnosis of epilepsy based on Electroencephalography (EEG) analysis is a beneficial practice which adopts machine learning to increase the recognition rate and saves physicians from long hours of EEG inspection. However multi-channel epilepsy EEG signals reflect significant nonlinearity with different degrees of cross-talk among channels, which further leads to high dimensional features extracted from EEG. These shortcomings make the performance of epilepsy detection with machine learning difficult to improve. In order to get fast and accurate detection performance, a feature dimension reduction algorithm based on epilepsy locality preserving projections (E-LPP) is proposed. E-LPP, by preserving the low-dimensional manifold as much as possible, enables to analyze signals of non-linear, non-stationary and high-dimensional nature. To get the best performance, we determine the hyperparameters of E-LPP by grid search. Subsequently a fusion epilepsy detection framework combined feature extraction with E-LPP is proposed to classify whether subjects’ seizure onset or not. We test our method on two well-known and widely studied datasets which includes ictal and interictal EEG recordings. The experimental result on recall, precision and F1 is superior to the common traditional dimensionality reduction algorithm, manifold learning algorithm and autoencoder based deep learning, which indicates this proposed method not only makes it possible to solve nonlinearity and cross-talk among channels in EEG, but also tackles the inherent difficulties regarding unbalanced epilepsy data with high metrics.
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The authors would like to thank the National Key Research and Development Program of China under grant No. 2017YFB1002504, National Natural Science Foundation of China under grant No. 41601353, National Science Foundation for Young Scientists of China under grant No. 61902317 and 61801384,the Natural Science Basic Research Plan in Shaanxi Province of China (2020JM-415), the Science and Technology Plan Program in Shaanxi Province of China under Grant (No. 2019JQ-166) for supporting.
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Yang Liu and Bo Jiang contributed equally to this work.
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Liu, Y., Jiang, B., Feng, J. et al. Classification of EEG Signals for Epileptic Seizures Using Feature Dimension Reduction Algorithm based on LPP. Multimed Tools Appl (2020). https://doi.org/10.1007/s11042-020-09135-7
- Wavelet packet decomposition
- Manifold learning
- Machine learning