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Detection of Drowsiness Using the Pulse Rate Variability of Finger

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Abstract

This paper proposes a new method for detecting a driver's drowsiness from changes in the pulse rate of a finger. Electroencephalographic (EEG) signals were received from a sample of ten individuals. The analysis of electroencephalographic signals is investigated and the activation of theta waves during the drowsiness interval is observed. Photoplethysmography signals were received from ten participants simultaneously with the EEG signal. Finger pulse data are analyzed and the features showing a significant change during drowsiness in the pulse rate variability (PRV) of the finger are further identified and extracted. The results indicate that the values and the average values of PRV increase before the point of sleep onset (SO). It is observed that the standard deviation of all PP intervals (SDNN) has a significant reduction during drowsiness. An increase in the values of RMSSD is also observed in the drowsiness interval. Besides, the ratio of low to high frequency (LF/HF) representing the balance of sympathetic and parasympathetic branches decreases in the vicinity of SO which indicates raised parasympathetic activity. The nonlinear analysis of the Poincaré plot indicated the reduction of the SD1 parameter. The results indicate that the PRV method can be used for the detection of driver drowsiness. Finally, the presented method is considered in an intelligent steering wheel design as a feasible non-invasive procedure for the detection of driver drowsiness.

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References

  1. Daza IG, Bergasa LM, Bronte S, Yebes JJ, Almazán J, Arroyo R. Fusion of optimized indicators from Advanced Driver Assistance Systems (ADAS) for driver drowsiness detection. Sensors. 2014;14(1):1106–31.

    Article  Google Scholar 

  2. Firke OK, Jain M. Analysis of driver drowsiness detection using EEG and EOG. Int J Eng Technol. 2018;7(2.17):46–51.

    Article  Google Scholar 

  3. He W. Application heart rate variability to driver fatigue detection of dangerous chemicals vehicles. In: 2014 Fifth international conference on intelligent systems design and engineering applications. IEEE; 2014. pp. 218–221.

  4. Jeyhani V, Mahdiani S, Peltokangas M, Vehkaoja A.Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2015. pp. 5952–5955.

  5. Jujhavarapu S, Babaeian M, Mozumdar M. Designing non-contact based ECG system for driver drowsiness detection. Int J Comput Digit Syst. 2018;7(04):199–205.

    Article  Google Scholar 

  6. Jung SJ, Shin HS, Chung WY. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intel Transport Syst. 2014;8(1):43–50.

    Article  Google Scholar 

  7. Lee J, Kim J, Shin M. Correlation analysis between electrocardiography (ECG) and photoplethysmogram (PPG) data for driver’s drowsiness detection using noise replacement method. Proc Comput Sci. 2017;116:421–6.

    Article  Google Scholar 

  8. Mahachandra M, Sutalaksana IZ, Suryadi K. Sensitivity of heart rate variability as indicator of driver sleepiness. In: 2012 Southeast Asian Network of Ergonomics Societies Conference (SEANES). IEEE; 2012. pp 1–6..

  9. Michail E, Kokonozi A, Chouvarda I, Maglaveras N. EEG and HRV markers of sleepiness and loss of control during car driving. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2008. pp. 2566–2569.

  10. Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability: a review on studies comparing photoplethysmographic technology with an electrocardiogram. Int J Cardiol. 2013;166(1):15–29.

    Article  Google Scholar 

  11. Shinar Z, Akselrod S, Dagan Y, Baharav A. Autonomic changes during wake–sleep transition: a heart rate variability based approach. Auton Neurosci. 2006;130(1–2):17–27.

    Article  Google Scholar 

  12. Sun Y, Yu X. An innovative nonintrusive driver assistance system for vital signal monitoring. IEEE J Biomed Health Inform. 2014;18(6):1932–9.

    Article  MathSciNet  Google Scholar 

  13. Taouli SA, Bereksi-Reguig F. Nonlinear analysis of the heart rate variability. In: 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT). IEEE; 2012. pp. 721–725.

  14. Wong JS, Lu WA, Wu KT, Liu M, Chen GY, Kuo CD. A comparative study of pulse rate variability and heart rate variability in healthy subjects. J Clin Monit Comput. 2012;26(2):107–14.

    Article  Google Scholar 

  15. Yu E, He D, Su Y, Zheng L, Yin Z, Xu L. Feasibility analysis for pulse rate variability to replace heart rate variability of the healthy subjects. In: 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE; 2013. pp. 1065–1070.

  16. Yu S, Wang B, Wang Y, Wang X. Feature extraction of vigilance level based on heart rate variability of electrocardiogram. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), Vol. 2. IEEE; 2011. pp. 753–756.

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Acknowledgements

The authors would like to acknowledge the staff of the Occupational Sleep Research Center and Sleep Clinic of Baharloo hospital.

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Authors and Affiliations

  1. University of Science and Technology, Tehran, Iran

    Saeideh Heydari & Ahmad Ayatollahi

  2. Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, P.O. Box 1339973111, Tehran, Iran

    Arezu Najafi

  3. Highway and Transportation Engineering at Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Mohammad Poorjafari

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  1. Saeideh Heydari
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  2. Ahmad Ayatollahi
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  3. Arezu Najafi
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  4. Mohammad Poorjafari
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Correspondence to Saeideh Heydari.

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Heydari, S., Ayatollahi, A., Najafi, A. et al. Detection of Drowsiness Using the Pulse Rate Variability of Finger. SN COMPUT. SCI. 3, 359 (2022). https://doi.org/10.1007/s42979-022-01247-1

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  • DOI: https://doi.org/10.1007/s42979-022-01247-1

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