Artificial neural nets for K-complex detection

An explorative study was initiated to determine whether artificial neural nets (ANNs) can be used to detect K-complexes in EEGs (electroencephalograms). K-complexes are relatively large waves with a duration of between 500 and 1500 ms often seen during sleep stage 2. Sleep spindles (bursts of rhythmic activity with a frequency of 12 to 16 Hz) are almost always observed in the neighborhood of K-complexes. The data and methods used to analyze K-complex are described. In all cases, a multilayer backpropagation ANN was used. The number of input nodes and hidden layers varied. Two different strategies were used to prepare the input to the ANN, and results for both are presented. The results indicate that the neural net approaches used are not adequate for the detection of K-complexes.<
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