Shallow Classifier with Sampling for Sleep Stage Classification of Autism Patients

  • Authors

    • Intan Nurma Yulita
    • Mohamad Ivan Fanany
    • Aniati Murni Arymurthy
    https://doi.org/10.14419/ijet.v7i4.36.29000
  • Classification, Sleep Stage, Autism, Imbalance Class, Machine Learning

  • Autism is a brain development disorder that affects the patient's ability to communicate and interact with others. Most people with autism get sleep disorders. But they have some difficulty to communicate, so this problem is getting worse. The alternative that can be done is to detect sleep disorders through polysomnography. One of the test purposes is to classify the sleep stages. The doctors need a long time to process it. This paper presents an automatic sleep stage classification. The classification was based on the shallow classifiers, namely naive Bayes, k-nearest neighbor (KNN), multi-layer perceptron (MLP), and C4.5 (a type of decision tree). On the other hand, this dataset has a class imbalance problem. As a solution, this study carried out the mechanism of resampling. The results show that the use of d as a measure of the uniformity of data distribution greatly influenced the classification performance. The higher d, the more uniform the distribution of data (0 <= d <= 1). The performance with d = 1 was higher than d = 0. On the other hand, KNN was the best classifier. The highest accuracy and F-measure were 83.07 and 82.80 respectively.

     

     

  • References

    1. [1] Aboalayon, K., Faezipour, M., Almuhammadi, W., & Moslehpour, S. (2016). Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy , 272.

      [2] Berry, R. B. (2012 ). Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. Journal of clinical sleep medicine 8(05), 597-619.

      [3] Boostani, R., Karimzadeh, F., & Nami, M. (2017). A comparative review on sleep stage classification methods in patients and healthy individuals. Computer methods and programs in biomedicine, 77-91.

      [4] Chung, K., Song, K., Cho, S., & Chang, J. (2018). Noncontact Sleep Study Based on an Ensemble of Deep Neural Network and Random Forests. IEEE Sensors Journal.

      [5] Devnani, P., & Hegde, A. (2015). Autism and sleep disorders. Journal of pediatric neurosciences 10(4).

      [6] Estabrooks, A. J. (2004). A multiple resampling method for learning from imbalanced data sets. Computational intelligence, 20(1), 18-36.

      [7] Fadhlullah, M., Resahya, A., Nugraha, D., & Yulita, I. (2018). Sleep stages identification in patients with sleep disorder using k-means clustering. Journal of Physics: Conference Series , 1013(1), 1-5.

      [8] Fernández-Varela, I., Hernández-Pereira, E., Ãlvarez-Estévez, D., & & Moret-Bonillo, V. (2017). Combining machine learning models for the automatic detection of EEG arousals. Neurocomputing (268), 100-108.

      [9] Fullagar, H., Skorski, S., Duffield, R., Hammes, D., Coutts, A., & Meyer, T. (2015). Sleep and athletic performance: the effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Medicine , 161-186.

      [10] Gouveris, H. e. (2017). Sleep stage classification using spectral analyses and support vector machine algorithm on C3-and C4-EEG signals. Sleep Medicine, 40, e116.

      [11] Haba-Rubio, J. &. (2012). Evaluation instruments for sleep disorders: A brief history of polysomnography and sleep medicine. Introduction to modern sleep technology, 19-31.

      [12] Hartzler, B. (2014). Fatigue on the flight deck: the consequences of sleep loss and the benefits of nappin. Accident Analysis & Prevention, 309-318.

      [13] Herrmann, S. (2016). Counting sheep: sleep disorders in children with autism spectrum disorders. Journal of Pediatric Health Care, 143-154.

      [14] Lajnef, T. e. (2015). Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. Journal of neuroscience methods , 250, 94-105.

      [15] Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 11-24.

      [16] Mirjalili, S. M. (2014). Let a biogeography-based optimizer train your multi-layer perceptron. Information Sciences, 269, 188-209.

      [17] Tian, J. M. (2016). Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Transactions on Industrial Electronics 63(3), 1793-1803.

      [18] Yulita, I. N., Fanany, M., & Arymurthy, A. (2018). Fast Convolutional Method for Automatic Sleep Stage Classification. Healthcare informatics research, 24(3), 170-178.

      [19] Yulita, I., Fanany, M., & Arymurthy, A. (2017a). Bi-directional Long Short-Term Memory using Quantized data of Deep Belief Networks for Sleep Stage Classification. ICCSCI (pp. 530-538). Bali: Procedia Computer Science.

      [20] Yulita, I., Fanany, M., & Arymurthy, A. (2017b). Sleep stage classification using convolutional neural networks and bidirectional long short-term memory. 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 303-308). Jakarta: IEEE.

      [21] Zhou, X. W. (2015). etection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier. International conference on bioinformatics and biomedical engineering (pp. 201-209). Springer.

  • Downloads

  • How to Cite

    Nurma Yulita, I., Ivan Fanany, M., & Murni Arymurthy, A. (2018). Shallow Classifier with Sampling for Sleep Stage Classification of Autism Patients. International Journal of Engineering & Technology, 7(4.36), 1419-1422. https://doi.org/10.14419/ijet.v7i4.36.29000