Automated seizure detection using limited-channel EEG and non-linear dimension reduction

Electroencephalography (EEG) is an essential component in evaluation of epilepsy. However, full-channel EEG signals recorded from 18 –23 electrodes on the scalp is neither wearable nor computationally effective. This paper presents advantages of both channel selection and nonlinear dimension reduction for accurate automatic seizure detection. We first extract the frequency domain features from the full-channel EEG signals. Then , we use a random forest algorithm to determine which channels contribute the most in discriminating seizure from non-seizure events.
Source: Computers in Biology and Medicine - Category: Bioinformatics Authors: Source Type: research