Research Area:  Machine Learning
Visual inspection of epileptic electroencephalogram (EEG) by neurologists is time-consuming and tedious. To overcome the problems, numerous automated seizure detection techniques, combining signal processing and machine learning, have been developed. Although 100% accuracy has been achieved for classifying non-seizure and seizure EEG records in up-to-date articles, the result of distinguishing normal, interictal and ictal EEG is still not satisfying. In this paper, a fusion method of variational mode decomposition (VMD) and autoregression (AR) based quadratic feature extraction was proposed for feature extraction and the random forest classifier was employed to hand with three-classification task. The raw EEG was decomposed into a fixed number of band-limited intrinsic mode functions (BLIMFs) using VMD, then a logarithmic operation was imposed on each BLIMF. Subsequently, optimal AR based quadratic feature extraction was conducted on all the BLIMFs and the extracted feature vectors were fed into random forest classifier for classification. Experimental results on the Bonn epilepsy EEG dataset show that the best accuracy of the proposed scheme is 97.352% and it outperforms than the fixed-order AR based feature extraction technique. The developed technology is proven efficient for seizure detection. It can be further programmed into software and the software can be applied in hospitals to assist the neurologists for seizure detection.
Author(s) Name:  Tao Zhang,Wanzhong Chen,Mingyang Li
Journal name:  Biomedical Signal Processing and Control
Publisher name:  Elsevier
Volume Information:  Volume 31, January 2017, Pages 550-559
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S1746809416301525