Automated classification of neonatal sleep states using EEG

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. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.Peer reviewe

Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants bornPeer reviewe

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

Peer reviewe

Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.Peer reviewe

Detecció automàtica i robusta de Bursts en EEG de nounats amb HIE. Enfocament tensorial

[ANGLÈS] Hypoxic-Ischemic Encephalopathy (HIE) is an important cause of brain injury in the newborn, and can result in long-term devastating consequences. Burst-suppression pattern is one of several indicators of severe pathology in the EEG signal that may occur after brain damage caused by e.g. asphyxia around the time of birth. The goal of this thesis is to design a robust method to detect burst patterns automatically regardless of the physiologic and extra-physiologic artifacts that may occur at any time. At first, a pre-detector has been designed to obtain potential burst candidates from different patients. Then, a post-classification has been implemented, applying high dimensional feature extraction methods, to get the real burst patterns from these patients with a high sensitivity.[CASTELLÀ] La Hipoxia-Isquemia Encefálica (HIE) es una causa importante de lesión cerebral en los recién nacidos, pudiendo acarrear devastadoras consecuencias a largo plazo. El patrón Burst-Suppression es uno de los indicadores dados en patologías severas en señales EEG los cuales ocurren después de una lesión cerebral causada, por ejemplo, por una asfixia poco después del nacimiento. El objetivo de esta tésis es diseñar un método robusto que detecte automáticamente patrones Burst, prescindiendo de los artefactos fisiológicos y extra-fisiológicos que puedan aparecer en cualquier momento. Primeramente, se ha diseñado un pre-detector para obtener los candidatos potenciales a Burst provenientes de diferentes pacientes. Seguidamente, se ha implementado una post-clasificación, aplicando métodos de extracción de características para altas dimensiones, para obtener patrones reales de Burst con una alta sensitividad.[CATALÀ] La Hipòxia-Isquèmia Encefàlica (HIE) és una causa important de lesió cerebral en nounats, que poden comportar devastadores conseqüències a llarg termini. El patró Burst-Suppression és un dels indicadors donats en patologies severes en els senyals EEG els quals ocorren després d'una lesió cerebral causada, per exemple, per una asfixia poc després del naixement. L'objectiu d'aquesta tesis és dissenyar un mètode robust que detecti automàticament patrons Burst, prescindint dels artefactes fisiològics i extra-fisiològics que poden aparèixer en qualsevol moment. Primerament, s'ha dissenyat un pre-detector per obtenir els candidats potencials a Burst provinents de diferents pacients. Seguidament, s'ha implementat una post-classificació, aplicant mètodes d'extracció de característiques per a altes dimensions, per tal d'obtenir patrons reals de Burst amb una alta sensitivitat

Scalp high-frequency oscillations differentiate neonates with seizures from healthy neonates

OBJECTIVE We aimed to investigate (1) whether an automated detector can capture scalp high-frequency oscillations (HFO) in neonates and (2) whether scalp HFO rates can differentiate neonates with seizures from healthy neonates. METHODS We considered 20 neonates with EEG-confirmed seizures and four healthy neonates. We applied a previously validated automated HFO detector to determine scalp HFO rates in quiet sleep. RESULTS Etiology in neonates with seizures included hypoxic-ischemic encephalopathy in 11 cases, structural vascular lesions in 6, and genetic causes in 3. The HFO rates were significantly higher in neonates with seizures (0.098 ± 0.091 HFO/min) than in healthy neonates (0.038 ± 0.025 HFO/min; P = 0.02) with a Hedge's g value of 0.68 indicating a medium effect size. The HFO rate of 0.1 HFO/min/ch yielded the highest Youden index in discriminating neonates with seizures from healthy neonates. In neonates with seizures, etiology, status epilepticus, EEG background activity, and seizure patterns did not significantly impact HFO rates. SIGNIFICANCE Neonatal scalp HFO can be detected automatically and differentiate neonates with seizures from healthy neonates. Our observations have significant implications for neuromonitoring in neonates. This is the first step in establishing neonatal HFO as a biomarker for neonatal seizures