Title :
Detection of epileptiform spikes in the EEG using a patient-independent neural network
Author :
Wilson, Kerry ; Webber, W. Robert S ; Lesser, Ronald P. ; Fisher, Robert S. ; Eberhart, Russell C. ; Dobbins, Roy W.
Author_Institution :
Johns Hopkins Univ. Sch. of Med., Baltimore, MD, USA
Abstract :
An offline neural network that successfully detects spikes when trained on multiple patients selected from a database of electroencephalogram (EEG) records with spikes marked by experienced electroencephalographers has been developed. This spike detector uses a simple threshold detector to identify potential spikes that appear on four-channel bipolar chains within the montage, and then passes waveform parameters to a three-layer neural network for second-level detection. Results obtained for the neural network with output thresholds arbitrarily set of 0.5 have yielded sensitivities averaging 74% and selectivities averaging 54%. While the selectivities for these trials were only fair, it is noted that substantial improvements could be achieved by raising the output thresholds
Keywords :
computerised monitoring; electroencephalography; medical computing; neural nets; database; electroencephalogram; epileptiform spikes; four-channel bipolar chains; multiple patients; offline neural network; patient-independent neural network; second-level detection; spike detector; three-layer neural network; threshold detector; waveform parameters; Computerized monitoring; Detectors; Electroencephalography; Epilepsy; Hospitals; Intelligent networks; Medical diagnostic imaging; Morphology; Neural networks; Physics;
Conference_Titel :
Computer-Based Medical Systems, 1991. Proceedings of the Fourth Annual IEEE Symposium
Conference_Location :
Baltimore, MD
Print_ISBN :
0-8186-2164-8
DOI :
10.1109/CBMS.1991.128978
