Neonatal EEG sleep stage classification based on deep learning and HMM
- 26 May 2020
- journal article
- research article
- Published by IOP Publishing in Journal of Neural Engineering
- Vol. 17 (3), 036031
- https://doi.org/10.1088/1741-2552/ab965a
Abstract
Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates. The classification performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38–40 weeks. A comprehensive set of linear and nonlinear features were extracted from thirty-second EEG segments. The feature space dimensionality was then reduced by using an evolutionary feature selection method called MGCACO (Modified Graph Clustering Ant Colony Optimization) based on the relevance and redundancy analysis. A bi-directional long-short time memory (BiLSTM) network was trained for sleep stage classification. The number of channels was optimized using the sequential forward selection method to reduce the spatial space. Finally, an HMM-based postprocessing stage was used to reduce false positives by incorporating the knowledge of transition probabilities between stages into the classification process. The method performance was evaluated using the K-fold (KFCV) and leave-one-out cross-validation (LOOCV) strategies. Using six-bipolar channels, our method achieved a mean kappa and an overall accuracy of 0.71-0.76 and 78.9%-82.4% using the KFCV and LOOCV strategies, respectively. The presented automatic sleep stage scoring method can be used to study the neurodevelopmental process and to diagnose brain abnormalities in term neonates.Funding Information
- Cognitive Science and Technology Council (CSTC) of Iran (1896, 3465)
- Bijzonder Onderzoeksfonds KU Leuven (C24/15/036)
- IWT Leuven Belgium (TBM 110697-NeoGuard)
- The Wellcome Trust (095802/B/110Z)
This publication has 45 references indexed in Scilit:
- American Clinical Neurophysiology Society Standardized EEG Terminology and Categorization for the Description of Continuous EEG Monitoring in NeonatesJournal of Clinical Neurophysiology, 2013
- A transition-constrained discrete hidden Markov model for automatic sleep stagingBioMedical Engineering OnLine, 2012
- Optimal channel selection for analysis of EEG-sleep patterns of neonatesComputer Methods and Programs in Biomedicine, 2012
- Electroencephalography in premature and full-term infants. Developmental features and glossaryNeurophysiologie Clinique/Clinical Neurophysiology, 2010
- Automatic classification of background EEG activity in healthy and sick neonatesJournal of Neural Engineering, 2010
- Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm NeonatesJournal of Medical Systems, 2009
- Automated detection of neonate EEG sleep stagesComputer Methods and Programs in Biomedicine, 2009
- A multistage knowledge-based system for EEG seizure detection in newborn infantsClinical Neurophysiology, 2007
- Automated neonatal seizure detection: A multistage classification system through feature selection based on relevance and redundancy analysisClinical Neurophysiology, 2006
- Prognostic Value of Background Patterns in the Neonatal EEGJournal of Clinical Neurophysiology, 1993