Automated detection of epileptogenic EEG spikes

Previous attempts at automatic detection of epileptogenic spikes in the EEG involved minicomputer-based systems which are not portable and not cost-effective. The latest advancements in microprocessor technology provide the means to develop a cost-effective, portable, and multi-microprocessor-based system that can implement sophisticated detection algorithms and can process 8 to 16 EEG channels at speeds up to 8X real time. This thesis project deals with the design and development of a single-channel version of such a multi-microprocessor system. The system processes signals in three processors: slave, master and multichannel processor. The slave computes the parameters that describe the spike morphology, compares them to predetermined thresholds to determine the occurrence of a spike, and sends the results to the multichannel processor (MCP) via the master. The MCP is responsible for the overall detection process and executes multichannel detection algorithms to determine the occurrence and position of the spike. The system processes signals at real time and 8X real time speeds, and allows modular expansion to become an 8-channel system. From the test data run with the system it was observed that the system spike detection performance is satisfactory.