title: Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals.
abstract: An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).
authors.affiliation: University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 680-749, Republic of Korea. Electronic address: amirtjoleng@yahoo.co.id.
authors.forename: Amir
authors.initials: A
authors.lastname: Tjolleng
authors.affiliation: University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 680-749, Republic of Korea. Electronic address: kjung@ulsan.ac.kr.
authors.forename: Kihyo
authors.initials: K
authors.lastname: Jung
authors.affiliation: LIG Nex1, 333 Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea. Electronic address: wonkiman@naver.com.
authors.forename: Wongi
authors.initials: W
authors.lastname: Hong
authors.affiliation: Delft University of Technology, Landbergstraat 15, Delft, 2628CE, Netherlands. Electronic address: w.lee@tudelft.nl.
authors.forename: Wonsup
authors.initials: W
authors.lastname: Lee
authors.affiliation: Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, 790-784, Republic of Korea. Electronic address: x200won@postech.ac.kr.
authors.forename: Baekhee
authors.initials: B
authors.lastname: Lee
authors.affiliation: Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, 790-784, Republic of Korea. Electronic address: hcyou@postech.ac.kr.
authors.forename: Heecheon
authors.initials: H
authors.lastname: You
authors.affiliation: Daegu Gyeongbuk Institute of Science and Technology, 333 Techno Jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu, 711-873, Republic of Korea. Electronic address: json@dgist.ac.kr.
authors.forename: Joonwoo
authors.initials: J
authors.lastname: Son
authors.affiliation: Korea Air Force Academy, PO Box 335-2, 635 Danjae-ro, Sangdang-gu, Cheongju, Choongbuk, 360-060, Republic of Korea. Electronic address: ergoparks@gmail.com.
authors.forename: Seikwon
authors.initials: S
authors.lastname: Park
mesh_terms: D000328:Adult; D001334:Automobile Driving / Q000523:psychology*; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D004562:Electrocardiography; D006339:Heart Rate; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D016526:Workload / Q000145:classification*; D055815:Young Adult
keywords: Artificial neural network; Cognitive workload classification; Heart rate variability