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

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.