Ninah Koolen1, Lisa Oberdorfer2, Zsofia Rona2, Vito Giordano2, Tobias Werther2, Katrin Klebermass-Schrehof2, Nathan Stevenson1, Sampsa Vanhatalo3. 1. BABA Center, Department of Children's Clinical Neurophysiology, Children's Hospital, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Finland. 2. Medical University Vienna, Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Vienna, Austria. 3. BABA Center, Department of Children's Clinical Neurophysiology, Children's Hospital, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Finland. Electronic address: sampsa.vanhatalo@helsinki.fi.
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 45weeks 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.
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 45weeks 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.
Authors: Anneleen Dereymaeker; Kirubin Pillay; Jan Vervisch; Maarten De Vos; Sabine Van Huffel; Katrien Jansen; Gunnar Naulaers Journal: Early Hum Dev Date: 2017-07-12 Impact factor: 2.079
Authors: Chuen Wai Lee; Borja Blanco; Laura Dempsey; Maria Chalia; Jeremy C Hebden; César Caballero-Gaudes; Topun Austin; Robert J Cooper Journal: Front Neurosci Date: 2020-04-17 Impact factor: 4.677
Authors: N J Stevenson; L Oberdorfer; N Koolen; J M O'Toole; T Werther; K Klebermass-Schrehof; S Vanhatalo Journal: Sci Rep Date: 2017-10-11 Impact factor: 4.379