Publication year
2011Source
Physiological Measurement, 32, 10, (2011), pp. 1623-37ISSN
Publication type
Article / Letter to editor
Display more detailsDisplay less details
Organization
Paediatrics - OUD tm 2017
Neurology
Journal title
Physiological Measurement
Volume
vol. 32
Issue
iss. 10
Page start
p. 1623
Page end
p. 37
Subject
DCN 2: Functional Neurogenomics; IGMD 1: Functional imagingAbstract
To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB(R)) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% +/- 6%, 80% +/- 9% and 97% +/- 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% +/- 8%, 96% +/- 3% and 85% +/- 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.
This item appears in the following Collection(s)
- Academic publications [246216]
- Faculty of Medical Sciences [93266]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.