Shahnawaz QURESHI, Sirirut VANICHAYOBON, Seppo KARRILA

GACNN SleepTuneNet: a genetic algorithm designing the convolutional neural network architecture for optimal classification of sleep stages from a single EEG channel

This study presents a method for designing–by a genetic algorithm, without manual intervention–the featurelearning architecture for classification of sleep stages from a single EEG channel, when using a convolutional neuralnetwork called GACNN SleepTuneNet. Two EEG electrode positions were selected, namely FP2-F4 and FPz-Cz, fromtwo available datasets. Twenty-five generations were involved in diagnosis without hand-crafted features, to learn thearchitecture for classification of sleep stages based on AASM standard. Based on the results, our model not only achievedthe highest classification accuracy, but it also distinguished the sleep stages based on either of the two EEG electrodesignals, in both datasets. The results show that our model performed the best with highest overall accuracy rates andkappa statistic (CAP sleep: 95.61% and 0.94; Sleep EDF: 92.51% and 0.90) among other state-of-the-art methods thatrequire no manual intervention. Our model could automatically learn the features for classification of sleep stages, fordifferent raw EEG electrode positions in different datasets, without user-assisted feature extraction.

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[1] Cecotti H, Eckstein MP, Giesbrecht B. Single-trial classification of event-related potentials in rapid serial visual presentation tasks using supervised spatial filtering. IEEE Transactions on Neural Networks and Learning Systems 2014; 25(11): 2030-2042.
[2] Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Transactions on Biomedical Engineering. 2016; 63(3): 664-675.
[3] Zhu X, Zheng WL, Lu BL, Chen X, Chen S, Wang C. EOG-based drowsiness detection using convolutional neural networks. In: International Conference on Neural Network; Beijing, China; 2014. pp. 128-134.
[4] Hossain D, Capi G, Jindai M. Optimizing deep learning parameters using genetic algorithm for object recognition and robot grasping. Journal of Electronic Science and Technology 2018; 16(1): 11-15.
[5] David OE, Greental I. Genetic algorithms for evolving deep neural networks. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation; Vancouver, BC, Canada; 2014. pp. 1451-1452.
[6] Lajnef T, Chaibi S, Ruby P, Aguera PE, Eichenlaub JB et al. Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. Journal of Neuroscience Methods 2015; 30 (250): 94-105.
[7] Huang CS, Lin CL, Ko LW, Liu SY, Su TP et al. Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels. Frontiers in Neuroscience 2014; 8: 263.
[8] Güneş S, Polat K, Yosunkaya Ş. Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Systems with Applications. 2010; 37(12): 7922-7928.
[9] Tsinalis O, Matthews PM, Guo Y. Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Annals of Biomedical Engineering. 2016; 44(5): 1587-1597.
[10] Tsinalis O, Matthews PM, Guo Y. Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Annals of Biomedical Engineering. 2016; 44(5): 1587-1597.
[11] Hassan AR, Subasi A. A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowledge-Based Systems 2017; 15(128):115-124.
[12] Qureshi S, Seppo K, Sirirut V. Human sleep scoring based on K-Nearest Neighbors. Turkish Journal of Electrical Engineering and Computer Sciences 2018; 26(6): 2802-2818.
[13] Boostani R, Karimzadeh F, Nami M. A comparative review on sleep stage classification methods in patients and healthy individuals. Computer Methods and Programs in Biomedicine 2017; 140: 77-91.
[14] Şen B, Peker M, Çavuşoğlu A, Çelebi FV. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. Journal of Medical Systems. 2014; 38(3): 18.
[15] Diykh M, Li Y. Complex networks approach for EEG signal sleep stages classification. Expert Systems with Applications. 2016; 63: 241-248.
[16] Hassan AR, Bhuiyan MI. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomedical Signal Processing and Control. 2016; 24: 1-10.
[17] Hassan AR, Bhuiyan MI. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybernetics and Biomedical Engineering. 2016; 36(1): 248-255.
[18] da Silveira TL, Kozakevicius AJ, Rodrigues CR. Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain. Medical Biological Engineering and Computing. 2017; 55(2): 343-352.
[19] Längkvist M, Karlsson L, Loutfi A. Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems. 2012; 1: 5.
[20] Tsinalis O, Matthews PM, Guo Y, Zafeiriou S. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. arXiv preprint arXiv:1610.01683. 2016.
[21] Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet: a model for automatic sleep stage scoring based on raw singlechannel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017; 25(11): 1998-2008.
[22] Hsu YL, Yang YT, Wang JS, Hsu CY. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing. 2013; 15 (104): 105-114.
[23] Dong H, Supratak A, Pan W, Wu C, Matthews PM et al. Mixed neural network approach for temporal sleep stage classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2018; 26(2): 324-33.
[24] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000; 101(23): 215-220.
[25] Rechtschaffen A, Kales A. A Manual for Standardized Terminology, Techniques and Scoring System for Sleep Stages in Human Subjects. Brain Information Service. Los Angeles, CA, USA: National Institutes of Health, 1968.
[26] Iber C, Ancoli-Israel S, Chesson A, Quan SF. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Westchester, IL, USA: American Academy of Sleep Medicine; 2007.
[27] Xie AYL. Genetic CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV); Venice, Italy; 2018. pp. 1388-1397.
[28] Young SR, Rose DC, Karnowski TP, Lim SH, Patton RM. Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments; Austin, USA; 2015. pp. 4.
[29] Grefenstette J, Gopal R, Rosmaita B, Van Gucht D. Genetic algorithms for the traveling salesman problem. In: Proceedings of the First International Conference on Genetic Algorithms and Their Applications; Pittsburgh, USA; 1985. pp. 160-168.
[30] Loshchilov I, Hutter F. CMA-ES for hyperparameter optimization of deep neural networks. arXiv preprint arXiv:1604.07269. 2016.
[31] Goldberg DE, Holland JH. Genetic algorithms and machine learning. Machine Learning 1988; 3(2): 95-99.
[32] Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR. Deep learning for healthcare applications based on physiological signals: a review. Computer Methods and Programs in Biomedicine 2018; 161: 1-3.
[33] Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
[34] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 2014; 15(1): 1929-58.
[35] Van Doorn J. Analysis of deep convolutional neural network architectures. arXiv preprint 2014: 9-12.
[36] Yulita IN, Fanany MI, Arymurthy AM. Fast convolutional method for automatic sleep stage classification. Healthcare Informatics Research 2018; 24(3): 170-178.
[37] Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33(1): 159-174.