Artificial neural nets for K-complex detection

Author

Jansen, B.H.

Author_Institution

Dept. of Electr. Eng., Houston Univ., TX, USA

Volume

9

Issue

3

fYear

1990

Firstpage

50

Lastpage

52

Abstract

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.<>

Keywords

electroencephalography; medical diagnostic computing; neural nets; 0.5 to 1.5 s; 12 to 16 Hz; K-complex detection; artificial neural nets; electroencephalograms; hidden layers; input nodes; multilayer backpropagation ANN; rhythmic activity; sleep spindles; sleep stage; Artificial neural networks; Band pass filters; Cutoff frequency; Electroencephalography; Feedforward systems; Humans; Pattern matching; Prototypes; Sleep; Testing;

fLanguage

English

Journal_Title

Engineering in Medicine and Biology Magazine, IEEE

Publisher

ieee

ISSN

0739-5175

Type

jour

DOI

10.1109/51.59213

Filename

59213