EEG channels reduction using PCA to increase XGBoost's accuracy for stroke detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels.

Original languageEnglish
Title of host publicationInternational Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
Subtitle of host publicationProceedings of the 2nd International Symposium on Current Progress in Mathematics and Sciences 2016
EditorsKiki Ariyanti Sugeng, Djoko Triyono, Terry Mart
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735415362
DOIs
Publication statusPublished - 10 Jul 2017
Event2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016 - Depok, Jawa Barat, Indonesia
Duration: 1 Nov 20162 Nov 2016

Publication series

NameAIP Conference Proceedings
Volume1862
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
CountryIndonesia
CityDepok, Jawa Barat
Period1/11/162/11/16

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    Fitriah, N., Wijaya, S. K., Fanany, M. I., Badri, C., & Rezal, M. (2017). EEG channels reduction using PCA to increase XGBoost's accuracy for stroke detection. In K. A. Sugeng, D. Triyono, & T. Mart (Eds.), International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016: Proceedings of the 2nd International Symposium on Current Progress in Mathematics and Sciences 2016 [030128] (AIP Conference Proceedings; Vol. 1862). American Institute of Physics Inc.. https://doi.org/10.1063/1.4991232