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Integrated Prediction Method for Mental Illness with Multimodal Sleep Function Indicators

  • Wen-tao Tan
  • Hong Wang
  • Lu-tong Wang
  • Xiao-mei Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

Abstract

Sleep quality has great effect on physical and mental health. Severe insomnia will cause autonomic neurological dysfunction. For making good clinical decisions, it is crucial to extract features of sleep quality and accurately predict the mental illness. Prior studies have a number of deficiencies to be overcome. On the one hand, the selected features for sleep quality are not good enough, as they do not account for multisource and heterogeneous features. On the other hand, the mental illness prediction model does not work well and thus needs to be enhanced and improved. This paper presents a multi-dimensional feature extraction method and an ensemble prediction model for mental illness. First, we do correlation analysis for every indicators and sleep quality, and further select the optimal heterogeneous features. Next, we propose a combinational model, which is integrated by basic modules according to their weights. Finally, we perform abundant experiments to test our method. Experimental results demonstrate that our approach outperforms many state-of-the-art approaches.

Keywords

Mental illness Sleep quality Ensemble prediction Multimodal sleep function indicator 

Notes

Acknowledgments

This work is supported by the National Nature Science Foundation of China (No. 61672329, No. 61373149, No. 61472233, No. 61572300, No. 81273704), Shandong Provincial Project of Education Scientific Plan (No. ZK1437B010).

References

  1. 1.
    Smyth, C.: The Pittsburgh Sleep Quality Index (PSQI). J. Gerontol. Nurs. 25(12), 10 (1999)CrossRefGoogle Scholar
  2. 2.
    Mariman, A., Vogelaers, D., Hanoulle, I., et al.: Validation of the three-factor model of the PSQI in a large sample of chronic fatigue syndrome (CFS) patients. J. Psychosom. Res. 72(2), 111–113 (2012)CrossRefGoogle Scholar
  3. 3.
    Phillips, K.D., Sowell, R.L., Rojas, M., et al.: Physiological and psychological correlates of fatigue in HIV disease. Biol. Res. Nurs. 6(1), 59–74 (2004)CrossRefGoogle Scholar
  4. 4.
    Shin, H.Y., Han, H.J., Shin, D.J., et al.: Sleep problems associated with behavioral and psychological symptoms as well as cognitive functions in Alzheimer’s disease. J. Clin. Neurol. 10(3), 203–209 (2014)CrossRefGoogle Scholar
  5. 5.
    Gutiérrez-Tobal, G.C., Álvarez, D., Crespo, A., et al.: Multi-class adaboost to detect Sleep Apnea-Hypopnea Syndrome severity from oximetry recordings obtained at home. In: Global Medical Engineering Physics Exchanges, pp. 1–5. IEEE (2016)Google Scholar
  6. 6.
    Sim, D.Y.Y., Teh, C.S., Ismail, A.I.: Improved boosted decision tree algorithms by adaptive apriori and post-pruning for predicting obstructive Sleep Apnea. Adv. Sci. Lett. 24, 1680–1684 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wen-tao Tan
    • 1
  • Hong Wang
    • 1
  • Lu-tong Wang
    • 1
  • Xiao-mei Yu
    • 1
  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina

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