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Proceedings Paper

Automated sleep scoring and sleep apnea detection in children
Author(s): David P. Baraglia; Matthew J. Berryman; Scott W. Coussens; Yvonne Pamula; Declan Kennedy; A. James Martin; Derek Abbott
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Paper Abstract

This paper investigates the automated detection of a patient's breathing rate and heart rate from their skin conductivity as well as sleep stage scoring and breathing event detection from their EEG. The software developed for these tasks is tested on data sets obtained from the sleep disorders unit at the Adelaide Women's and Children's Hospital. The sleep scoring and breathing event detection tasks used neural networks to achieve signal classification. The Fourier transform and the Higuchi fractal dimension were used to extract features for input to the neural network. The filtered skin conductivity appeared visually to bear a similarity to the breathing and heart rate signal, but a more detailed evaluation showed the relation was not consistent. Sleep stage classification was achieved with and accuracy of around 65% with some stages being accurately scored and others poorly scored. The two breathing events hypopnea and apnea were scored with varying degrees of accuracy with the highest scores being around 75% and 30%.

Paper Details

Date Published: 16 January 2006
PDF: 12 pages
Proc. SPIE 6039, Complex Systems, 60390T (16 January 2006); doi: 10.1117/12.638867
Show Author Affiliations
David P. Baraglia, The Univ. of Adelaide (Australia)
Matthew J. Berryman, The Univ. of Adelaide (Australia)
Scott W. Coussens, The Univ. of Adelaide (Australia)
Yvonne Pamula, Women's and Children's Hospital (Australia)
Declan Kennedy, Women's and Children's Hospital (Australia)
A. James Martin, Women's and Children's Hospital (Australia)
Derek Abbott, The Univ. of Adelaide (Australia)


Published in SPIE Proceedings Vol. 6039:
Complex Systems
Axel Bender, Editor(s)

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