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Fuzzy-Logic Inference for Early Detection of Sleep Onset in Car Driver

  • Mario Malcangi
  • Salvatore Smirne
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

Heart rate variability (HRV) is an important sign because it reflects the activity of the autonomic nervous system (ANS), which controls most of the physiological activity of the subjects, including sleep. The balance between the sympathetic and parasympathetic branches of the nervous system is an effective indicator of heart rhythm and, indirectly, heart rhythm is related to a patient’s state of wakefulness or sleep. In this paper we present a research that models a fuzzy logic inference engine for early detection of the onset of sleep in people driving a car or a public transportation vehicle. ANS activity reflected in the HRV signal is measured by electrocardiogram (ECG). Power spectrum density (PSD) is computed from the HRV signal and ANS frequency activity is then measured. Crisp measurements such as very low, low, and high HRV and low-to-high frequency ratio variability are fuzzified and evaluated by a set of fuzzy-logic rules that make inferences about the onset of sleep in automobile drivers. An experimental test environment has been developed to evaluate this method and its effectiveness.

Keywords

onset sleep heart rate variability power spectrum density fuzzy logic autonomic nervous system 

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References

  1. 1.
    Dorfman Furman, G., Baharav, A., Cahan, C., Akselrod, S.: Early Detection of Falling Asleep at the Wheel: a Heart Rate Variability Approach. Computers in Cardiology 35, 1109–1112 (2008)Google Scholar
  2. 2.
    Zocchi, C., Giusti, A., Adami, A., Scaramellini, F., Rovetta, A.: Biorobotic System for Increasing Automotive Safety. In: 12th IFToMM World Congress, Besançon (France), June18-21 (2007)Google Scholar
  3. 3.
    Estrada, E., Nazeran, H.: EEG and HRV Signal Features for Automatic Sleep Staging and Apnea Detection. In: 20th International Conference on Electronics, Communications and Computer (CONIELECOMP), February 22-24, pp. 142–147 (2010)Google Scholar
  4. 4.
    Lewicke, A.T., Sazonov, E.S., Schuckers, S.A.C.: Sleep-wake Identification in Infants: Heart Rate Variability Compared to Actigraphy. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA, September 1-5, pp. 442–445 (2004)Google Scholar
  5. 5.
    Manis, G., Nikolopoulos, S., Alexandridi, A.: Prediction Techniques and HRV Analysis. In: MEDICON 2004, Naples, Italy, July 31-August 5 (2004)Google Scholar
  6. 6.
    Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C.M., Suri, J.S.: Heart Rate Variability: a Review. Med. Bio. Eng. Comput. 44, 1031–1051 (2006)CrossRefGoogle Scholar
  7. 7.
    Tohara, T., Katayama, M., Takajyo, A., Inoue, K., Shirakawa, S., Kitado, M., Takahashi, T., Nishimur, Y.: Time Frequency Analysis of Biological Signal During Sleep. In: SICE Annual Conference, September 17-20, pp. 1925–1929. Kagawa University, Japan (2007)CrossRefGoogle Scholar
  8. 8.
    Ranganathan, G., Rangarajan, R., Bindhu, V.: Evaluation of ECG Signal for Mental Stress Assessment Using Fuzzy Technique. International Journal of Soft Computing and Engineering (IJSCE) 1(4), 195–201 (2011)Google Scholar
  9. 9.
    Ranganathan, G., Rangarajan, R., Bindhu, V.: Signal Processing of Heart Rate Variability Using Wavelet Transform for Mental Stress Measurement. Journal of Theoretical and Applied Information Technology 11(2), 124–129 (2010)Google Scholar
  10. 10.
    Lewicke, A., Shuckers, S.: Sleep versus Wake Classification from Heart Rate Variability Using Computational Intelligence: Consideration of Rejection in Classification Models. IEEE Transactions on Biomedical Engineering 55(1), 108–117 (2008)CrossRefGoogle Scholar
  11. 11.
    Mager, D.E., Merritt, M.M., Kasturi, J., Witkin, L.R., Urdiqui-Macdonald, M., Sollers, J.I., Evans, M.K., Zonderman, A.B., Abernethy, D.R., Thayer, J.F.: Kullback–Leibler Clustering of Continuous Wavelet Transform Measures of Heart Rate Variability. Biomed. Sci. Instrum. 40, 337–342 (2004)Google Scholar
  12. 12.
    Alexandridi, A., Stylios, C., Manis, G.: Neural Networks and Fuzzy Logic Approximation and Prediction for HRV Analysis. In: Hybrid Systems and their Implementation on Smart Adaptive Systems, Oulu, Finland (2003)Google Scholar
  13. 13.
    Dzitac, S., Popper, L., Secui, C.D., Vesselenyi, T., Moga, I.: Fuzzy Algorithm for Human Drowsiness Detection Devices. SIC 19(4), 419–426 (2010)Google Scholar
  14. 14.
    Catania, D., Malcangi, M., Rossi, M.: Improving the Automatic Identification of Crackling Respiratory Sounds Using Fuzzy Logic. International Journal of Computational Intelligence 2(1), 8–14 (2004)Google Scholar
  15. 15.
    Azevedo de Carvalho, J.L., Ferreira da Rocha, A., Assis de Oliveira Nascimento, F., Souza Neto, J., Junqueira Jr., L.F.: Development of a Matlab Software for Analysis of Heart Rate Variability. In: 6th International Conference on Signal Processing Proceedings, pp. 1488–1491 (2002) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mario Malcangi
    • 1
  • Salvatore Smirne
    • 2
  1. 1.DICo - Dipartimento di Informatica e ComunicazioneUniversità degli Studi di MilanoMilanoItaly
  2. 2.Dipartimento di Tecnologie per la SaluteUniversità degli Studi di MilanoMilanoItaly

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