Detection of focal epileptiform events in the EEG by spatio-temporal dipole clustering.
OBJECTIVE
Methods for the detection of epileptiform events can be broadly divided into two main categories: temporal detection methods that exploit the EEG's temporal characteristics, and spatial detection methods that base detection on the results of an implicit or explicit source analysis. We describe how the framework of a spatial detection method was extended to improve its performance by including temporal information. This results in a method that provides (i) automated localization of an epileptogenic focus and (ii) detection of focal epileptiform events in an EEG recording. For the detection, only one threshold value needs to be set.


METHODS
The method comprises five consecutive steps: (1) dipole source analysis in a moving window, (2) automatic selection of focal brain activity, (3) dipole clustering to arrive at the identification of the epileptiform cluster, (4) derivation of a spatio-temporal template of the epileptiform activity, and (5) template matching. Routine EEG recordings from eight paediatric patients with focal epilepsy were labelled independently by two experts. The method was evaluated in terms of (i) ability to identify the epileptic focus, (ii) validity of the derived template, and (iii) detection performance. The clustering performance was evaluated using a leave-one-out cross validation. Detection performance was evaluated using Precision-Recall curves and compared to the performance of two temporal (mimetic and wavelet based) and one spatial (dipole analysis based) detection methods.


RESULTS
The method succeeded in identifying the epileptogenic focus in seven of the eight recordings. For these recordings, the mean distance between the epileptic focus estimated by the method and the region indicated by the labelling of the experts was 8mm. Except for two EEG recordings where the dipole clustering step failed, the derived template corresponded to the epileptiform activity marked by the experts. Over the eight EEGs, the method showed a mean sensitivity and selectivity of 92 and 77%, respectively.


CONCLUSIONS
The method allows automated localization of the epileptogenic focus and shows good agreement with the region indicated by the labelling of the experts. If the dipole clustering step is successful, the method allows a detection of the focal epileptiform events, and gave a detection performance comparable or better to that of the other methods.


CONCLUSIONS
The identification and quantification of epileptiform events is of considerable importance in the diagnosis of epilepsy. Our method allows the automatic identification of the epileptic focus, which is of value in epilepsy surgery. The method can also be used as an offline exploration tool for focal EEG activity, displaying the dipole clusters and corresponding time series.
Van Hese
Peter
P
Department of Electronics and Information Systems, Medical Image and Signal Processing, Ghent University-IBBT-IBiTech, De Pintelaan 185 Block B, B-9000 Ghent, Belgium. Electronic address: Peter.VanHese@UGent.be.
Vanrumste
Bart
B
Department of Electrical Engineering (ESAT/SCD), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; Katholieke Hogeschool Kempen, Kleinhoefstraat 4, B-2440 Geel, Belgium.
Hallez
Hans
H
Department of Electronics and Information Systems, Medical Image and Signal Processing, Ghent University-IBBT-IBiTech, De Pintelaan 185 Block B, B-9000 Ghent, Belgium.
Carroll
Grant J
GJ
Department of Neurology, Christchurch Hospital, Private Bag 4710, Christchurch, New Zealand.
Vonck
Kristl
K
Department of Neurology, Laboratory of Clinical and Experimental Neurophysiology, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium.
Jones
Richard D
RD
Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand; Department of Medical Physics and Bioengineering, Christchurch Hospital, Private Bag 4710, Christchurch, New Zealand; Department of Medicine, University of Otago (Christchurch), P.O. Box 4345, Christchurch, New Zealand; Van der Veer Institute for Parkinson's and Brain Research, P.O. Box 2682, Christchurch, New Zealand.
Bones
Philip J
PJ
Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
D'Asseler
Yves
Y
Department of Electronics and Information Systems, Medical Image and Signal Processing, Ghent University-IBBT-IBiTech, De Pintelaan 185 Block B, B-9000 Ghent, Belgium.
Lemahieu
Ignace
I
Department of Electronics and Information Systems, Medical Image and Signal Processing, Ghent University-IBBT-IBiTech, De Pintelaan 185 Block B, B-9000 Ghent, Belgium.
D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D004566:Electrodes; D004569:Electroencephalography; D004828:Epilepsies, Partial / Q000473:pathology / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors