Ebrahimian, Serajeddin ; Nahvi, Ali ; Tashakori, Masoumeh ; Salmanzadeh, Hamed ; Mohseni, Omid ; Leppänen, Timo (2022)
Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.
In: International Journal of Environmental Research and Public Health, 19 (17)
doi: 10.3390/ijerph191710736
Artikel, Bibliographie

Kurzbeschreibung (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.

Typ des Eintrags:	Artikel
Erschienen:	2022
Autor(en):	Ebrahimian, Serajeddin ; Nahvi, Ali ; Tashakori, Masoumeh ; Salmanzadeh, Hamed ; Mohseni, Omid ; Leppänen, Timo
Art des Eintrags:	Bibliographie
Titel:	Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks
Sprache:	Englisch
Publikationsjahr:	2022
Ort:	Darmstadt
Verlag:	MDPI
Titel der Zeitschrift, Zeitung oder Schriftenreihe:	International Journal of Environmental Research and Public Health
Jahrgang/Volume einer Zeitschrift:	19
(Heft-)Nummer:	17
Kollation:	17 Seiten
DOI:	10.3390/ijerph191710736
Zugehörige Links:	TUprints Zweitveröffentlichung
Kurzbeschreibung (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.
Freie Schlagworte:	ECG, respiration, deep learning, drowsiness detection, multi-level classification
Zusätzliche Informationen:	This article belongs to the Special Issue Applications of Artificial Intelligence to Health
Sachgruppe der Dewey Dezimalklassifikatin (DDC):	600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau
Fachbereich(e)/-gebiet(e):	03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Sportwissenschaft Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS)
Hinterlegungsdatum:	02 Aug 2024 12:43
Letzte Änderung:	02 Aug 2024 12:43
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• Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks. (deposited 12 Sep 2022 13:23)
  • Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks. (deposited 02 Aug 2024 12:43) [Gegenwärtig angezeigt]

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