Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

H. A. Adhi, Sastra Kusuma Wijaya, Prawito, Cholid Badri, M. Rezal

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

Original languageEnglish
Article number012005
JournalJournal of Physics: Conference Series
Volume820
Issue number1
DOIs
Publication statusPublished - 5 Apr 2017
Event5th International Conference on Science and Engineering in Mathematics, Chemistry and Physics 2017, ScieTech 2017 - Kuta, Bali, Indonesia
Duration: 21 Jan 201722 Jan 2017

Fingerprint Dive into the research topics of 'Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine'. Together they form a unique fingerprint.

  • Cite this