Identification of peak V in brainstem auditory evoked potentials with neural networks.
A feature extractor for determining the latency of peak V in brainstem auditory evoked potentials (BAEPs) is presented in this paper. A feature extractor that combines artificial neural networks with an algorithmic approach is presented. It consists of a series of small neural networks that have to make simple decisions. Each neural network decides what part of the input pattern contains the peak, and the algorithm passes that part of the pattern to the next neural network; in this way the size of the input patterns decreases during the process, and the last neural network determines the exact location of the peak. An optimal configuration of neural networks could determine the latencies of peak V in all synthetic evoked potentials correctly. With real evoked potentials, the networks yield results that comply with the opinion of a human expert in 80 +/- 6% of the cases.
Habraken
J B
JB
Division of Medical Electrical Engineering, Eindhoven University of Technology, The Netherlands.
van Gils
M J
MJ
Cluitmans
P J
PJ
D000328:Adult; D000465:Algorithms; D016477:Artifacts; D003661:Decision Support Techniques; D004569:Electroencephalography; D016057:Evoked Potentials, Auditory, Brain Stem / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011930:Reaction Time; D012815:Signal Processing, Computer-Assisted