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

Serajeddin Ebrahimian (), Ali Nahvi, Masoumeh Tashakori, Hamed Salmanzadeh, Omid Mohseni and Timo Leppänen
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Serajeddin Ebrahimian: Department of Applied Physics, University of Eastern Finland, 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
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

IJERPH, 2022, vol. 19, issue 17, 1-17

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.

Keywords: ECG; respiration; deep learning; drowsiness detection; multi-level classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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