ELM (Extreme Learning Machine) Method for Detecting Acute Ischemic Stroke using Conventional and Specific Asymmetry BSI (Brain Symmetry Index) features based on EEG Signals

摘要

Generally, acute ischaemic stroke (AIS) is diagnosed using MRI (Magnetic Resonance Imaging), CT (Computed Tomography) or fMRI (Functional MRI). However, MRI, fMRI, and CT are not available in community hospitals (C-type hospitals, PUSKESMAS). In addition, MRI, fMRI, and CT cannot measure for a long time or are unlikely to do continuous scanning. In most community hospitals, they have EEG (Electroencephalogram) machines to record brain waves. There are several methods available for detecting AIS, namely BSI (Brain symmetry Index), DAR (delta / alpha) and DTABR (delta + theta) / (alpha + beta) that analyze the power ratio of brain waves from the whole brain. These methods need to be refined. Therefore, authors attempt to use new method: specific asymmetry BSI. This method compares the frequencies not for 1-25 Hz like BSI method, but looking for specific frequency band and the power ratio of brainwave from right and left hemisphere. To develop a stroke detection system, author uses the algorithm Extreme Machine Learning (ELM) because ELM provides accurate data with high speed rather read by human eye. All data were obtained from RS PON (Rumah Sakit Pusat Otak Nasional), Jakarta in edf format. There were 66 voluntary subjects and analyzed with Matlab. The BSIs and specific asymmetry BSIs were calculated using pwelch methods, and the DARs and DTABRs were calculated using wavelet db4. The ELM algorithm was confirmed using CT-scan, which was diagnosed by qualified doctors. It is expected that this method would be useful for detecting AIS in community hospitals.