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

5th International Conference on Science & Engineering in Mathematics, Chemistry and Physics 2017

数学;化学;物理学

Adhi, H.A.^1 ; Wijaya, S.K.^2 ; Prawito^2 ; Badri, C.^1 ; Rezal, M.^1

Biomedical Engineering, Master Program, Universitas Indonesia, Jakarta, Indonesia^1

Faculty of Mathematics and Natural Sciences, Universitas Indonesia, West Java, Indonesia^2

关键词: Activation functions;    Automatic Detection;    Cerebrovascular disease;    Detrended fluctuation analyses (DFA);    Electro-encephalogram (EEG);    Extreme learning machine;    Ischemic strokes;    Sensitivity and specificity;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/820/1/012005/pdf
DOI  :  10.1088/1742-6596/820/1/012005

来源: IOP

【 摘 要 】

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.