Multichannel detection of epileptogenic EEG spikes
A multimicroprocessor-based system has been developed for the detection of epileptogenic spikes in four channels of the human electroencephalogram. The system is programmable, capable of operating at real-time and faster than real-time speeds, portable, and expandable to eight channels. It has been designed following a hierarchical architecture, whereby hybrid preprocessor modules serve for data reduction and preliminary spike detection at each channel, and a central processor module implements multichannel correlation and makes the final decision regarding the presence of a spike. This thesis work has involved specifically the design of the central (multichannel) processor and the integration of all the modules into a single stand-alone system. A multichannel detection algorithm has been developed, implementing heuristic features of the visual spike analysis, such as, time coincidence, surface polarity, and phase reversals. Techniques to eliminate false positive detections due to muscle artifact bursts have been included. Results are presented evaluating the performance of the detection system in the analysis of four-channel EEG recordings from two epileptic patients.