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|Title:||Automated sleep scoring and sleep apnea detection in children|
|Citation:||Proceedings of Microelectronics, MEMS, and Nanotechnology 2005, 2006 / Bender, A. (ed./s), vol.6039, pp.60930T-1-60930T-12|
|Series/Report no.:||Proceedings of SPIE--the International Society for Optical Engineering ; 6039|
|Conference Name:||SPIE Microelectronics, MEMS, and Nanotechnology (11 Dec 2005 - 14 Dec 2005 : Brisbane, Australia)|
|Baraglia, David P.; Berryman, Matthew J.; Coussens, Scott W.; Pamula, Yvonne; Kennedy, Declan; Martin, A. James; Abbott, Derek|
|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%.|
|Description:||Copyright © 2006 SPIE--The International Society for Optical Engineering|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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