IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p10736-d900484.html
   My bibliography  Save this article

Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks

Author

Listed:
  • Serajeddin Ebrahimian

    (Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland
    Virtual Reality Laboratory, K. N. Toosi University of Technology, Tehran 19697-6449, Iran
    Diagnostic Imaging Center, Kuopio University Hospital, 70210 Kuopio, Finland)

  • Ali Nahvi

    (Virtual Reality Laboratory, K. N. Toosi University of Technology, Tehran 19697-6449, Iran)

  • Masoumeh Tashakori

    (Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland
    Virtual Reality Laboratory, K. N. Toosi University of Technology, Tehran 19697-6449, Iran)

  • Hamed Salmanzadeh

    (Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran 19697-6449, Iran)

  • Omid Mohseni

    (Lauflabor Locomotion Lab, Institute of Sports Science, Centre for Cognitive Science, Technische Universität Darmstadt, 64283 Darmstadt, Germany)

  • Timo Leppänen

    (Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland
    Diagnostic Imaging Center, Kuopio University Hospital, 70210 Kuopio, Finland
    School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia)

Abstract

The high number of fatal crashes caused by driver drowsiness highlights the need for developing reliable drowsiness detection methods. An ideal driver drowsiness detection system should estimate multiple levels of drowsiness accurately without intervening in the driving task. This paper proposes a multi-level drowsiness detection system by a deep neural network-based classification system using a combination of electrocardiogram and respiration signals. The proposed method is based on a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for classifying drowsiness by concurrently using heart rate variability (HRV), power spectral density of HRV, and respiration rate signal as inputs. Two models, a CNN-based model and a hybrid CNN-LSTM-based model were used for multi-level classifications. The performance of the proposed method was evaluated on experimental data collected from 30 subjects in a simulated driving environment. The performance and the results of both models are presented and compared. The best performance for both three-level and five-level drowsiness classifications was achieved by the CNN-LSTM model. The results indicate that the three-level and five-level classifications of drowsiness can be achieved with 91 and 67% accuracy, respectively.

Suggested Citation

  • Serajeddin Ebrahimian & Ali Nahvi & Masoumeh Tashakori & Hamed Salmanzadeh & Omid Mohseni & Timo Leppänen, 2022. "Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10736-:d:900484
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/10736/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/17/10736/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sadegh Arefnezhad & Arno Eichberger & Matthias Frühwirth & Clemens Kaufmann & Maximilian Moser & Ioana Victoria Koglbauer, 2022. "Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals," Energies, MDPI, vol. 15(2), pages 1-25, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.
    2. Arno Eichberger & Marianne Kraut & Ioana V. Koglbauer, 2022. "Improved Perception of Motorcycles by Simulator-Based Driving Education," Sustainability, MDPI, vol. 14(9), pages 1-16, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10736-:d:900484. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.