Spatial analysis of multi-channel EEG recordings through a fuzzy-rule based system in the detection of epileptiform events

A system has been developed which utilises fuzzy logic to perform spatial analysis of the multichannel EEG recording and forms the final stage of a multi-stage system to detect the presence of epileptiform events (EVs) in the EEG. This spatial-combiner consists of a set of 127 fuzzy-rules which define one's expectation of the spatial distribution of an EV as measured across a 4-channel bipolar chain of scalp electrodes. A set of probabilities assigned to each channel by the previous ANN-based stage of the EV-detection system are fuzzified into 5 fuzzy variables and the best matching fuzzy rule gives an output of either definite, probable or possible to indicate a detection of an EV on spatial grounds. The system was tested on 8 clinical EEG recordings (7 epileptiform and 1 normal) which indicated a sensitivity of 55.3%, a selectivity of 82.0% and a false detection rate of 7.2/hour. These results show a 50-fold decrease in the false detection rate when compared to the performance of the system without spatial analysis, whilst maintaining a comparable level of sensitivity.

[1]  A J Gabor,et al.  Automated interictal EEG spike detection using artificial neural networks. , 1992, Electroencephalography and clinical neurophysiology.

[2]  Richard D. Jones,et al.  The self-organising feature map in the detection of epileptiform transients in the EEG , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  J. Frost,et al.  Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives , 1989, IEEE Transactions on Biomedical Engineering.

[4]  J Gotman,et al.  Automatic recognition of interictal spikes. , 1985, Electroencephalography and clinical neurophysiology. Supplement.

[5]  Christopher J. James,et al.  Detection of epileptiform activity in the electroencephalogram using artificial neural networks , 1997 .

[6]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[7]  W.R. Fright,et al.  A multistage system to detect epileptiform activity in the EEG , 1993, IEEE Transactions on Biomedical Engineering.

[8]  Richard D. Jones,et al.  A system for detecting epileptiform discharges in the EEG: real-time operation and clinical trial , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[10]  W R Webber,et al.  Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. , 1994, Electroencephalography and clinical neurophysiology.