A Study on Spike Detection and Classification from Epileptic EEG Data

  • 劉 勇均

Student thesis: Doctoral Thesis


Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data In this thesis a new two–stage approach is proposed for epileptic spike detection First the k-point nonlinear energy operator (k-NEO) is used to detect all possible spike candidates Then different kinds of features are extracted and applied to these candidates for spike classification Moment descriptors are first applied as the features to describe the EEG candidate data and the empirical mode decomposed candidate data for spike classification The statistical moments give promising classification results however the moment method does not include the shape information which is critical for epileptic spike classification We subsequently propose a novel spike model-based method for spike classification Although spikes with slow waves frequently occur in epileptic EEGs they are not used in conventional spike detection The newly proposed system accommodates both the single spike and spike with slow wave in the spike model Using the AdaBoost classifier the system outperforms the conventional spike model in both two- and three-class EEG classification problems It not only achieves better accuracy in spike classification but provides new ability to differentiate between spikes and spikes with slow waves Consequently the proposed system has better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis
Date of Award2014 Feb 13
Original languageEnglish
SupervisorYung-Nien Sun (Supervisor)

Cite this