A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels

In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres where only a limited number of EEG channels are recorded. Existing automated algorithms mainly use multi-channel EEGs which perform poorly when fewer numbers of channels are available. The proposed Sinc utilizes multi-scale analysis to place emphasis on the temporal EEG information to be less dependent on the number of EEG channels. In Sinc, we increase the receptive fields through Inception while by additionally sharing the filters that have similar receptive fields, overfitting is controlled and the number of trainable parameters dramatically reduced. To train and test this model, 96 longitudinal EEG recordings from 26 premature infants are used. The Sinc-based model significantly outperforms state-of-the-art neonatal quiet sleep detection algorithms, with mean Kappa 0.77 ± 0.01 (with 8-channel EEG) and 0.75 ± 0.01 (with a single bipolar channel EEG). This is the first study using Inception-based networks for EEG analysis that utilizes filter sharing to improve efficiency and trainability. The suggested network can successfully detect quiet sleep stages with even a single EEG channel making it more practical especially in the hospital setting where cerebral function monitoring is predominantly used.

[1]  Marina De Vos,et al.  Neonatal EEG sleep stage classification based on deep learning and HMM , 2020, Journal of neural engineering.

[2]  M. de Vos,et al.  Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes , 2020, Scientific Reports.

[3]  Luay Fraiwan,et al.  Neonatal sleep stage identification using long short-term memory learning system , 2020, Medical & Biological Engineering & Computing.

[4]  Sabine Van Huffel,et al.  Decomposition of a Multiscale Entropy Tensor for Sleep Stage Identification in Preterm Infants , 2019, Entropy.

[5]  Vincenzo Piuri,et al.  Deep-ECG: Convolutional Neural Networks for ECG biometric recognition , 2019, Pattern Recognit. Lett..

[6]  Sabine Van Huffel,et al.  Neonatal Seizure Detection Using Deep Convolutional Neural Networks , 2019, Int. J. Neural Syst..

[7]  U. Rajendra Acharya,et al.  Classification of myocardial infarction with multi-lead ECG signals and deep CNN , 2019, Pattern Recognit. Lett..

[8]  Oliver Y. Chén,et al.  SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Sabine Van Huffel,et al.  Quiet sleep detection in preterm infants using deep convolutional neural networks , 2018, Journal of neural engineering.

[10]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[11]  Sabine Van Huffel,et al.  Automated EEG sleep staging in the term-age baby using a generative modelling approach , 2018, Journal of neural engineering.

[12]  Federico Baldassarre,et al.  Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2 , 2017, ArXiv.

[13]  Sabine Van Huffel,et al.  Review of sleep-EEG in preterm and term neonates. , 2017, Early human development.

[14]  Sabine Van Huffel,et al.  Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification , 2017, Entropy.

[15]  Sabine Van Huffel,et al.  Automatic quiet sleep detection based on multifractality in preterm neonates: Effects of maturation , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Xiaoling Xia,et al.  Inception-v3 for flower classification , 2017, 2017 2nd International Conference on Image, Vision and Computing (ICIVC).

[17]  S. Vanhatalo,et al.  Automated classification of neonatal sleep states using EEG , 2017, Clinical Neurophysiology.

[18]  Sabine Van Huffel,et al.  An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation , 2017, Int. J. Neural Syst..

[19]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[21]  Marina De Vos,et al.  Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor , 2016, Clinical Neurophysiology.

[22]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[24]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[25]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  L. Fraiwan,et al.  Newborn sleep stage identification using multiscale entropy , 2014, 2nd Middle East Conference on Biomedical Engineering.

[29]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Sheng-Fu Liang,et al.  Automatic stage scoring of single-channel sleep EEG based on multiscale permutation entropy , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[31]  Hubert Cecotti,et al.  Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Luay Fraiwan,et al.  Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates , 2011, Journal of Medical Systems.

[33]  Kenneth A. Loparo,et al.  Automated detection of neonate EEG sleep stages , 2009, Comput. Methods Programs Biomed..

[34]  Perumpillichira J. Cherian,et al.  Technical standards for recording and interpretation of neonatal electroencephalogram in clinical practice , 2009, Annals of Indian Academy of Neurology.

[35]  Manuel Alonso,et al.  Receptive field , 2009, Scholarpedia.

[36]  James Allan,et al.  A comparison of statistical significance tests for information retrieval evaluation , 2007, CIKM '07.

[37]  Lenka Lhotská,et al.  Neonatal EEG Sleep Stages Modelling by Temporal Profiles , 2007, EUROCAST.

[38]  Vladimir Krajca,et al.  Newborn Sleep Stage Classification Using Hybrid Evolutionary Approach , 2007 .

[39]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[40]  B. Connolly,et al.  Concurrent Validity of the Bayley Scales of Infant Development II (BSID-II) Motor Scale and the Peabody Developmental Motor Scale II (PDMS-2) in 12-Month-Old Infants , 2006, Pediatric physical therapy : the official publication of the Section on Pediatrics of the American Physical Therapy Association.

[41]  Aatif M Husain,et al.  Review of Neonatal EEG , 2005, American journal of electroneurodiagnostic technology.

[42]  L. Lhotska,et al.  Automatic Detection of Sleep Stages in Neonatal EEG Using the Structural Time Profiles , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[43]  J. S. Barlow,et al.  Computer characterization of tracé alternant and REM sleep patterns in the neonatal EEG by adaptive segmentation--an exploratory study. , 1985, Electroencephalography and clinical neurophysiology.

[44]  J. Fleiss,et al.  Statistical methods for rates and proportions , 1973 .