TY - GEN
T1 - Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity
AU - Rahma, Osmalina N.
AU - Wijaya, Sastra K.
AU - Prawito,
AU - Badri, Cholid
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/29
Y1 - 2017/11/29
N2 - Stroke is one of the highest causes of death in adults and disability in Indonesia, even in the world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate prognosis assessment to improve stroke management. This study tried to automatically classify AIS severity based on EEG signals by using digital signal processing such as Wavelet transform and feedforward type of neural network with ELM algorithm. In this study, Delta Alpha Ratio (DAR), (Delta+Theta)/(Alpha+Beta) Ratio (DTABR) and Brain Symmetry Index (BSI)'s value were used as the ELM input feature score, which were obtained by using Wavelet transformation (Daubechies 4) and Welch's method to classify the acute ischemic stroke severity which refers to the National Institutes of Health Stroke Scale (NIHSS). It had shown that the performance of system test accuracy, the sensitivity and specificity were above 72%. These results were useful for classifying AIS based on EEG signals.
AB - Stroke is one of the highest causes of death in adults and disability in Indonesia, even in the world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate prognosis assessment to improve stroke management. This study tried to automatically classify AIS severity based on EEG signals by using digital signal processing such as Wavelet transform and feedforward type of neural network with ELM algorithm. In this study, Delta Alpha Ratio (DAR), (Delta+Theta)/(Alpha+Beta) Ratio (DTABR) and Brain Symmetry Index (BSI)'s value were used as the ELM input feature score, which were obtained by using Wavelet transformation (Daubechies 4) and Welch's method to classify the acute ischemic stroke severity which refers to the National Institutes of Health Stroke Scale (NIHSS). It had shown that the performance of system test accuracy, the sensitivity and specificity were above 72%. These results were useful for classifying AIS based on EEG signals.
KW - Acute ischemic stroke (AIS)
KW - Electroenchepalogram (EEG)
KW - Extreme learning machine (ELM)
UR - http://www.scopus.com/inward/record.url?scp=85043448686&partnerID=8YFLogxK
U2 - 10.1109/ISSIMM.2017.8124287
DO - 10.1109/ISSIMM.2017.8124287
M3 - Conference contribution
AN - SCOPUS:85043448686
T3 - Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017
SP - 180
EP - 186
BT - Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017
Y2 - 25 August 2017 through 26 August 2017
ER -