Neural-network techniques for visual mining clinical electroencephalograms
In this chapter, we describe new neural-network techniques developed for visual mining clinical electroencephalograms (EEGs), the weak electrical potentials invoked by brain activity. These techniques exploit the fruitful ideas of Group Method of Data Handling (GMDH). Section 2 briefly describes the standard neural-network techniques that are able to learn well-suited classification modes from data presented by relevant features. Section 3 introduces an evolving cascade neural network technique that adds new input nodes as well as new neurons to the network while the training error decreases. This algorithm is applied to recognize artifacts in the clinical EEGs. Section 4 presents the GMDH-type polynomial networks trained from data. We applied this technique to distinguish the EEGs recorded from an Alzheimer and a healthy patient as well as recognize EEG artifacts. Section 5 describes the new neural-network technique developed to derive multi-class concepts from data. We used this technique for deriving a 16-class concept from the large-scale clinical EEG data. Finally, we discuss perspectives of applying the neuralnetwork techniques to clinical EEGs
Key words: Classification model, pattern visualization, neural network, cascade architecture, feature selection, polynomial, electroencephalogram, decision tree
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