Sleep classification in infants based on artificial neural networks.
The study reports on the possibility of classifying sleep stages in infants using an artificial neural network. The polygraphic data from 4 babies aged 6 weeks, 6 months and 1 year recorded over 8 hours were available for classification. From each baby 22 signals were recorded, digitized and stored on an optical disc. Subsets of these signals and additional calculated parameters were used to obtain data vectors, each of which represents an interval of 30 sec. For classification, two types of neural networks were used, a Multilayer Perceptron and a Learning Vector Quantizer. The teaching input for both networks was provided by a human expert. For the 6 sleep classes in babies aged 6 months, a 65% to 80% rate of correct classification (4 babies) was obtained for the testing data not previously seen.
Pfurtscheller
G
G
Ludwig-Boltzmann-Institute of Medical Informatics and Neuroinformatics, Graz University of Technology.
Flotzinger
D
D
Matuschik
K
K
D002540:Cerebral Cortex / Q000503:physiopathology; D004569:Electroencephalography / Q000145:classification* / Q000295:instrumentation; D005583:Fourier Analysis; D006801:Humans; D007223:Infant; D016571:Neural Networks, Computer; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012891:Sleep Apnea Syndromes / Q000145:classification* / Q000503:physiopathology; D012894:Sleep Stages / Q000502:physiology*