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Automatic Detection of Sleep Stages based on Accelerometer Signals from a Wristband

  • Yeo, Minsoo (Department of Computer Engineering, Kwangwoon University) ;
  • Koo, Yong Seo (Department of Neurology, Korea University Medical Center, Anam Hospital, Korea University College of Medicine) ;
  • Park, Cheolsoo (Department of Computer Engineering, Kwangwoon University)
  • Received : 2017.01.16
  • Accepted : 2017.02.06
  • Published : 2017.02.28

Abstract

In this paper, we suggest an automated sleep scoring method using machine learning algorithms on accelerometer data from a wristband device. For an experiment, 36 subjects slept for about eight hours while polysomnography (PSG) data and accelerometer data were simultaneously recorded. After the experiments, the recorded signals from the subjects were preprocessed, and significant features for sleep stages were extracted. The extracted features were classified into each sleep stage using five machine learning algorithms. For validation of our approach, the obtained results were compared with PSG scoring results evaluated by sleep clinicians. Both accuracy and specificity yielded over 90 percent, and sensitivity was between 50 and 80 percent. In order to investigate the relevance between features and PSG scoring results, information gains were calculated. As a result, the features that had the lowest and highest information gain were skewness and band energy, respectively. In conclusion, the sleep stages were classified using the top 10 significant features with high information gain.

Keywords

References

  1. Morillo, Daniel Sanchez, "An accelerometer-based device for sleep apnea screening." IEEE transactions on information technology in biomedicine, vol. 14, no. 2, pp. 491-499, 2010. https://doi.org/10.1109/TITB.2009.2027231
  2. Karantonis, Dean M., "Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring." IEEE transactions on information technology in biomedicine, vol 10, No. 1, pp. 156-167, 2006. https://doi.org/10.1109/TITB.2005.856864
  3. Jean-Louis, Girardin, "Sleep estimation from wrist movement quantified by different actigraphic modalities." Journal of neuroscience methods, vol 105, No. 2, pp. 185-191, 2001. Article (CrossRefLink) https://doi.org/10.1016/S0165-0270(00)00364-2
  4. Witten, Ian H., and Eibe Frank. "Data Mining: Practical machine learning tools and techniques." Morgan Kaufmann 2005. Article, vol 2005.
  5. Cleary, John G., Leonard E. Trigg. "K*: An instancebased learner using an entropic distance measure." Proceedings of the 12th International Conference on Machine learning, vol 5, pp. 108-114, 1995
  6. Dahiya, Shashi, S. S. Handa, and N. P. Singh. "Impact of bagging on MLP classifier for credit evaluation." Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on. IEEE, 2016, vol. 2016, pp. 3794-3800, 2016
  7. Lira, Milde MS. "Combining multiple artificial neural networks using random committee to decide upon electrical disturbance classification." 2007 International Joint Conference on Neural Networks. IEEE, 2007, vol. 2007, pp. 2863-2868, 2007.
  8. Hosseini, Mohammad-Parsa, Abolfazl Hajisami, and Dario Pompili. "Real-time Epileptic Seizure Detection from EEG Signals via Random Subspace Ensemble Learning." Autonomic Computing (ICAC), 2016 IEEE International Conference on. IEEE, 2016, vol. 2016, pp. 209-218, 2016.
  9. Liaw, Andy, and Matthew Wiener. "Classification and regression by randomForest." R news, vol. 2, no. 3, pp. 18-22, 2002.
  10. Dai, Jianhua, and Qing Xu. "Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification." Applied Soft Computing, vol. 13, no. 1, pp. 211-221, 2013. https://doi.org/10.1016/j.asoc.2012.07.029