Characterization of Electroencephalography of strokes based on Time-frequency analysis

Abstract Stroke is a medical condition, under which poor blood flow to the brain results in cell death. Electroencephalography, EEG, is an electrophysiological monitoring method to record electrical activity of the brain. If EEG signal is processed properly, the feature of each individual stroke case can be analysed to indicate location and severity, which could be used for the diagnosis and continuous monitoring of the treatment process in clinic. In this work, EEG signals from 11 patients with ischemic stroke and 1 healthy person are analysed by Short-time Fourier Transformation (STFT). All patients have malignant middle cerebral artery infarction or received reperfusion therapy. EEG data from ten of them are investigated, which indicates that the delta frequency band energy percentage (DFBEP) in the ipsilateral hemisphere is always higher than the contralateral side, while the alpha frequency band energy percentage (AFBEP) and alpha/delta (AD) are the opposite. Based on this feature, one EEG signal collected along 15 hours from the 11 th patient is further analysed by STFT. Compared to the medical record of this patient, the energy of each frequency content changes with the progress of treatment and recovery of patient accordingly, which could be used to monitor the patient condition during the treatment.