NDLI: Automated Sleep-Wake Detection in Neonates from Cerebral Function Monitor Signals

Content Provider	IEEE Xplore Digital Library
Author	Eklund, J.M. Fontana, N. Pugh, E. McGregor, C. Yielder, P. James, A. Keyzers, M. Hahn, C. McNamara, P.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr., Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada (Eklund, J.M.) \|\| Dept. of Paediatrics, Univ. of Toronto, Toronto, ON, Canada (James, A.; Keyzers, M.; Hahn, C.; McNamara, P.) \|\| Fac. of Health Sci., Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada (Yielder, P.) \|\| Fac. of Bus. & Inf. Technol., Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada (Fontana, N.; Pugh, E.; McGregor, C.)
Abstract	Amplitude-integrated electroencephalography (aEEG), a time compression technique, compresses the time scale of the conventional electroencephalogram (EEG) which is advantageous for presenting long EEG recordings. Cerebral function monitors use a reduced number of electrodes from the standard 10-20 montage and displays both an EEG and aEEG trace from one or two channels. Sleep-wake cycling is defined as a state of continuous normal voltage and the presence of both wakefulness or active sleep and quiet sleep with a minimum of two or three consecutive sleep state changes on aEEG for a duration of twenty minutes during a three-four hour period. Sleep-wake cycling in infants is often used as an indicator of the patient's neurological development and response to brain injury. There has been complete absence of algorithm development for the automated detection of changes in normal neonatal sleep-wake cycling patterns displayed by cerebral function monitor. This study will incorporate a unique, robust algorithm for incorporation into a multidimensional data analysis environment, which is capable of capturing multiple individual streams of physiological data from bedside monitors and processing them. The framework supports the acquisition, collection, transmission, real-time processing, storage and retrospective analysis of wave form and physiological data streams combined with supporting clinical information including clinical observations and the results of laboratory investigations. The development of a robust, accurate and reliable algorithm that detects sleep-wake cycling in newborn infants greater than 29 weeks gestational age should provide valuable information for meaningful clinical decision support for physicians caring for critically ill neonates. This paper describes the development and results of the algorithm, its upper and lower boundary detection, low pass filtration, and threshold classification on an individual patient's data set.
Starting Page	22
Ending Page	27
File Size	449242
Page Count	6
File Format	PDF
ISBN	9781479944354
ISSN	23729198
DOI	10.1109/CBMS.2014.36
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pediatrics Electroencephalography Sleep Monitoring Biomedical monitoring Real-time systems Algorithm design and analysis sleep-wake detection cerebral function monitor amplitude-integrated electroencephalography
Content Type	Text
Resource Type	Article

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