Automated classification of neonatal sleep states using EEG. Issue 6 (June 2017)

• Record Type: Journal Article
• Authors: Koolen, Ninah; Oberdorfer, Lisa; Rona, Zsofia; Giordano, Vito; Werther, Tobias; Klebermass-Schrehof, Katrin; Stevenson, Nathan; Vanhatalo, Sampsa
• Is Part Of: Clinical neurophysiology. Volume 128:Issue 6(2017:Jun.)
• Page Start: 1100
• Page End: 1108
• Publication Date: 2017-06
• Subjects: EEG electroencephalography -- NICU neonatal intensive care unit -- AS active sleep -- QS quiet sleep -- SSP sleep state probability -- IBI inter-burst interval -- SAT spontaneous activity transient -- LL line length -- NLEO non-linear energy operator -- HI histogram index -- ASI activation synchrony index -- SVM support vector machine; Neonatal EEG -- Brain monitoring -- Sleep-wake cycling -- Classification -- Support vector machine; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2017.02.025
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Neonatal EEG and its sleep state characteristics change across development. The combination of EEG features allows sleep state classification across preterm development. The classifier allows the generation of a sleep state probability index for long term monitoring. Abstract: Objective: To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age. Methods: We collected 231 EEG recordings from 67 infants between 24 and 45 weeks of postmenstrual age. Ten minute epochs of 8 channel polysomnography ( N = 323) from active and quiet sleep were used as a training dataset. We extracted a set of 57 EEG features from the time, frequency, and spatial domains. A greedy algorithm was used to define a reduced feature set to be used in a support vector machine classifier. Results: Performance tests showed that our algorithm was able to classify quiet and active sleep epochs with 85% accuracy, 83% sensitivity, and 87% specificity. The performance was not substantially lowered by reducing the epoch length or EEG channel number. The classifier output was used to construct a novel trend, the sleep state probability index, that improves the visualisation of brain state fluctuations. Conclusions: A robust EEG-based sleep state classifier was developed. It performs consistently well across a large span of postmenstrual ages. Significance: This method enables the visualisation of sleep state in preterm infants which can assist clinical management in the neonatal intensive care unit.