conference paper

Detection of epileptiform spikes in the EEG using a patient-independent neural network

K. Wilson^a, William R. Webber^b, R. P. Lesser^a, Robert S. Fisher^a, Russell C. Eberhart^c, Roy W. Dobbins^c

^aJohns Hopkins University School of Medicine, Baltimore, MD, USA
^bJohn Hopkins Hospital, Baltimore, MD, USA
^cJohns Hopkins University-Applied Physics Laboratory, Laurel, MD, USA

[1991] Computer-Based Medical Systems@m_Proceedings of the Fourth Annual IEEE Symposium2002-12-10 15 citations4 references

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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.>

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Detection of epileptiform spikes in the EEG using a patient-independent neural network

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