Title :
A constrained approach for extraction of pre-ictal discharges from scalp EEG
Author :
Shapoori, Shahrzad ; Wenwu Wang ; Sanei, Saeid
Author_Institution :
Fac. of Eng. & Phys. Sci., Univ. of Surrey, Guildford, UK
Abstract :
A constrained blind source separation (BSS) approach for separation of intracranial spikes from scalp electroencephalogram (EEG) has been proposed in this paper. This method is based on creating a template from intracranial data, which is then used in the form of a constraint in a BSS algorithm. To generate a suitable template, the segments during which the brain discharges are labelled are used to generate the necessary templates. Approximate entropy followed by peak detection and thresholding is used for this purpose. Constrained BSS is then applied to scalp data to extract the desired source and to evaluate its effect on scalp electrodes. The effectiveness of such a constrained approach has been demonstrated by comparing its outcome with that of the unconstrained method.
Keywords :
approximation theory; biomedical electrodes; blind source separation; electroencephalography; feature extraction; medical signal processing; BSS algorithm; approximate entropy; brain discharges; constrained blind source separation; intracranial data; intracranial spikes separation; peak detection; preictal discharges extraction; scalp EEG; scalp data; scalp electrodes; scalp electroencephalogram; thresholding; Cost function; Discharges (electric); Electroencephalography; Entropy; Fault location; Scalp; EEG; approximate entropy; constrained BSS; interictal discharges; intracranial recording;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on
Conference_Location :
Southampton
DOI :
10.1109/MLSP.2013.6661929