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

Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces

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

Electroencephalogram data platform for application of reduction methods

Long-term electroencephalogram (EEG) monitoring (≥24-h) is a resourceful tool for properly diagnosis sparse life-threatening events like non-convulsive seizures and status epilepticus in Intensive Care Unit (ICU) inpatients. Such EEG data requires objective methods for data reduction, transmission and analysis. This work aims to assess specificity and sensibility of HaEEG and aEEG methods in combination with conventional multichannel EEG when achieving seizure detection. A database architecture was designed to handle the interoperability, processing, and analysis of EEG data. Using data from CHB-MIT public EEG database, the reduced signal was obtained by EEG envelope segmentation, with 10 and 90 percentiles obtained for each segment. The use of asymmetrical filtering (2-15 Hz) and overall clinical band (1-70 Hz) was compared. The upper and lower margins of compressed segments were used to classify ictal and non-ictal epochs. Such classification was compared with the corresponding specialist seizure annotation for each patient. The difference between medians of instantaneous frequencies of ictal and non-ictal periods were assessed using Wilcoxon Rank Sum Test, which was significant for signals filtered from 2 to 15 Hz (p = 0.0055) but not for signals filtered from 1 to 70 Hz (p = 0.1816).O eletroencefalograma (EEG) de longa duração (≥24-h) em monitoramento contínuo é diferencial no diagnóstico e classificação de eventos epileptiformes, como crises não convulsivas e status epilepticus, em pacientes de Unidades de Tratamento Intensivo (UTI). Este exame requer métodos objetivos de análise, redução e transmissão de dados. O objetivo desse trabalho é avaliar a especificidade e a sensibilidade dos métodos HaEEG e aEEG em combinação com EEG multicanal convencional na detecção de eventos epileptiformes. Uma arquitetura de integração de dados foi projetada para gerir o armazenamento, processamento e análise de dados de EEG. Foram utilizados dados do banco de dados de EEG público do CHB-MIT. O sinal reduzido foi obtido pela segmentação do envelope do EEG, com percentis 10 e 90 obtidos para cada segmento. A aplicação do filtro assimétrico (2-15 Hz) e em bandas clínicas (1-70 Hz) foi comparada. Os limiares superiores e inferiores dos segmentos do aEEG e HaEEG foram usados para classificar épocas ictais e não ictais. A classificação foi comparada com as anotações feitas por um especialista para cada paciente. As medianas das frequências instantâneas para períodos ictais e não ictais foram analisadas com Wilcoxon Rank Sum Test com significância para filtragem assimétrica (p = 0,0055), mas não nas bandas clínicas (p = 0,1816)