Comparison of EMD, DWT and WPD for the localization of epileptogenic foci using Random Forest classifier
Abstract Localization of epileptogenic foci is an essential phase in surgical treatment planning using the earliest time detection of the seizure onset in the recordings of electroencephalogram (EEG). These recordings are defined as the areas of the brain which can be surgically removed to reach control of seizure. The characteristics of the brain area affected by partial epilepsy can be analyzed using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented to compare different feature extraction methods combined with multi-scale principal component analysis (MSPCA) denoising and Random Forest (RF) as a machine learning technique. After de-noising, different feature extraction methods (EMD, DWT and WPD) are applied. The performance of RF classifier is measured according to accuracy, the area under the Receiver Operating Characteristics (ROC) curve (AUC) and F-measure. EEG based focal region localization with Wavelet Packet Decomposition (WPD) feature extraction and RF classifier reach 99.92% accuracy. Hence, WPD combined with RF machine learning classifier can be used to differentiate the focal and non-focal EEG signals.