Electroencephalogram Analysis With Wavelet Transform And Neural Network As A Tool For Acute Ischemic Stroke Identification
The prevalence of stroke in Indonesia are 7% based on the health proffesionals statement and 12.1% based on
patients' symptoms' history. Early examination using CT scan causes radiation effects and spent high operational cost whilethe
prevalence of stroke based on diagnosis or symptoms were higher in the lowest (13.1 ‰) and mid lower (12.6 ‰)
quintile of ownership index. This studytried to analyze the signals of EEG automatically based ontraining data sets from
normal patients and patients with acute ischemic stroke (AIS) using digital signal processing such as wavelet transform and
feed forward type of neural network with Extreme Learning Machine (ELM) algorithm. It was claimed that electroencephalography
could help to confirm or detect acute ischemic stroke which is shown by the presence of the slow wave and the
asymmetrical wave of right and left hemisphere. This study uses Delta Alpha Ratio (DAR), Delta Theta Alpha Ratio
(DTABR) and Brain Symmetry Index (BSI)'s value as the ELM input feature score which were obtained by Wavelet (Daubechies
4) transformation and Welch's method to identify acute ischemic stroke. In this study, the performance of systemtest
accuracy, sensitivity and specificity were above 93%.
Keywords— Electroenchepalogram (EEG), Acute Ischemic Stroke, Wavelet Transform, Extreme Learning Machine (ELM).