Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold cross-validation. We used class-balanced random sampling within the stochastic gradient descent (SGD) optimization of the CNN to avoid skewed performance in favor of the most represented sleep stages. We achieved high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all subjects. By analyzing and visualizing the filters that our CNN learns, we found that rules learned by the filters correspond to sleep scoring criteria in the American Academy of Sleep Medicine (AASM) manual that human experts follow. Our method's performance is balanced across classes and our results are comparable to state-of-the-art methods with hand-engineered features. We show that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages.

[1]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[2]  Steve Renals,et al.  Convolutional Neural Networks for Distant Speech Recognition , 2014, IEEE Signal Processing Letters.

[3]  S. Chokroverty,et al.  The visual scoring of sleep in adults. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[4]  Sheng-Fu Liang,et al.  Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models , 2012, IEEE Transactions on Instrumentation and Measurement.

[5]  Aeilko H. Zwinderman,et al.  Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG , 2000, IEEE Transactions on Biomedical Engineering.

[6]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[7]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[8]  J. Mattout,et al.  Automatic analysis of single-channel sleep EEG: validation in healthy individuals. , 2007, Sleep.

[9]  Xiaoping Chen,et al.  EOG-based drowsiness detection using convolutional neural networks , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[10]  Ron J. Weiss,et al.  Speech acoustic modeling from raw multichannel waveforms , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Michael X Cohen,et al.  Analyzing Neural Time Series Data: Theory and Practice , 2014 .

[12]  Dimitri Palaz,et al.  Convolutional Neural Networks-based continuous speech recognition using raw speech signal , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  E. Wolpert A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[14]  Yan Song,et al.  Robust sound event recognition using convolutional neural networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  A. Rechtschaffen,et al.  A manual of standardized terminology, technique and scoring system for sleep stages of human subjects , 1968 .

[16]  Yan Wu,et al.  Convolutional deep belief networks for feature extraction of EEG signal , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[17]  Dimitri Palaz,et al.  Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks , 2013, INTERSPEECH.

[18]  Yifan Gong,et al.  An analysis of convolutional neural networks for speech recognition , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[20]  Benjamin Schrauwen,et al.  End-to-end learning for music audio , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

[22]  R. Foster,et al.  Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease , 2010, Nature Reviews Neuroscience.

[23]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[24]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[25]  Natheer Khasawneh,et al.  Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier , 2012, Comput. Methods Programs Biomed..

[26]  Kenneth J. Pope,et al.  Thinking activates EMG in scalp electrical recordings , 2008, Clinical Neurophysiology.

[27]  D. Rapoport,et al.  Interobserver agreement among sleep scorers from different centers in a large dataset. , 2000, Sleep.

[28]  Miguel P. Eckstein,et al.  Single-Trial Classification of Event-Related Potentials in Rapid Serial Visual Presentation Tasks Using Supervised Spatial Filtering , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[29]  I. Nelken,et al.  Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades , 2008, Neuron.

[30]  Tara N. Sainath,et al.  Deep convolutional neural networks for LVCSR , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[31]  Yike Guo,et al.  Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders , 2015, Annals of Biomedical Engineering.

[32]  J. Wolpaw,et al.  EMG contamination of EEG: spectral and topographical characteristics , 2003, Clinical Neurophysiology.

[33]  William H. Spriggs Essentials of Polysomnography: A Training Guide and Reference For Sleep Technicians , 2008 .