Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals

Amir Tjolleng, Kihyo Jung, Wongi Hong, Wonsup Lee, Baekhee Lee, Heecheon You, Joonwoo Son, Seikwon Park

Research output: Contribution to journalArticleScientificpeer-review

39 Citations (Scopus)


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%).
Original languageEnglish
Pages (from-to)326-332
Number of pages7
JournalApplied Ergonomics: human factors in technology and society
Publication statusPublished - 2017


  • Cognitive workload classification
  • Heart rate variability
  • Artificial neural network

Fingerprint Dive into the research topics of 'Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals'. Together they form a unique fingerprint.

Cite this