Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity

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

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology
Subtitle of host publicationInnovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages180-186
Number of pages7
ISBN (Electronic)9781538607459
DOIs
Publication statusPublished - 29 Nov 2017
Event2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017 - Surabaya, Indonesia
Duration: 25 Aug 201726 Aug 2017

Publication series

NameProceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017
Volume2017-January

Conference

Conference2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017
CountryIndonesia
CitySurabaya
Period25/08/1726/08/17

Keywords

  • Acute ischemic stroke (AIS)
  • Electroenchepalogram (EEG)
  • Extreme learning machine (ELM)

Fingerprint Dive into the research topics of 'Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity'. Together they form a unique fingerprint.

  • Cite this

    Rahma, O. N., Wijaya, S. K., Prawito, & Badri, C. (2017). Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. In Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017 (pp. 180-186). (Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSIMM.2017.8124287