TY - GEN
T1 - EEG channels reduction using PCA to increase XGBoost's accuracy for stroke detection
AU - Fitriah, N.
AU - Wijaya, Sastra Kusuma
AU - Fanany, Mohamad Ivan
AU - Badri, Cholid
AU - Rezal, M.
N1 - Publisher Copyright:
© 2017 Author(s).
PY - 2017/7/10
Y1 - 2017/7/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85026223890&partnerID=8YFLogxK
U2 - 10.1063/1.4991232
DO - 10.1063/1.4991232
M3 - Conference contribution
AN - SCOPUS:85026223890
T3 - AIP Conference Proceedings
BT - International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
A2 - Sugeng, Kiki Ariyanti
A2 - Triyono, Djoko
A2 - Mart, Terry
PB - American Institute of Physics Inc.
T2 - 2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
Y2 - 1 November 2016 through 2 November 2016
ER -