Determination of cognitive workload variation in driving from ECG derived respiratory signal and heart rate
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1
Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), TS2, France
Context
Research works on operator monitoring and affective computing underline the benefit of gathering several signal modalities to improve accuracy for an objective mental state diagnosis (Rahman, Begum and Ahmed, 2015). The cardiovascular activity is one of the most utilized systemic measures to assess Cognitive Workload (CW) and emotion. However, the respiration contribution is usually neglected or employed mainly for artifact rejection and scarcely utilized for CW estimation in ecological situations (Grassmann, Vlemincx, von Leupoldt, Mittelstäd and Van den Bergh, 2016).
On the other hand, from an ergonomic standpoint, the inclusion of new sensors to collect physiological data could be difficult to ensure high quality signals without hindering operator’s comfort, this is the case when driving smart vehicles.
Objectives
The purpose of this study is twofold. Firstly, it aims at verifying the advantage of incorporating Respiratory Rate (RR) as feature, with regard to Heart Rate (HR), so as to evaluate driver’s activity and CW variations. Secondly, this study aims at checking the feasibility and accuracy to extract RR from the ECG recordings in order to save on sensors.
Method
Eighteen healthy subjects (10 males, 22.7 ± 1.4 years) participated in the study. None of them had a history of cardiorespiratory diseases. A valid driving license for at least 3 years was required. The participants performed two different cognitive tasks, 5-minutes length approximately, requiring different CW demands: a beep counting for the Low Cognitive Workload (LCW) condition and a mental displacement within a previously memorized 5×5 numerical grid including an arithmetic task for the High Cognitive Workload (HCW). The participants carried out these cognitive tasks during two activities: a Single Task (ST) as well as simultaneously to a Driving Task (DT) in a simulator. They had to drive in an urban residential zone with sparse traffic.
The ECG and respiration signals were recorded by MP150 Biopac system. The ECG-derived respiratory signal was extracted by using Matlab R2017b according to the algorithm implemented by Moody, Mark, Zoccola and Mantero (1995), 4.5 minutes length segments were selected for feature extraction. An analysis of variance (ANOVA) of two factors, the first one representing the activity (ST and DT) and the second one referring to the CW demand (LCW and HCW) was performed for the statistical contrasts. Tukey correction was applied for post hoc analysis. A paired t-test and Pearson’s correlation were computed to estimate ECG-derived RR accuracy. All the statistical analyses were carried out by using SPSS 13.0 software.
Results
Concerning HR, a main effect of activity (F (1,11) = 21.2; p<.01; ɳ2=.658) and CW demand (F (1,11) = 32.68; p<.01; ɳ2=.748) were found, showing an increase in HR while driving in comparison to ST and for HCW. Their interaction was not significant. Similarly to HR, RR increased while driving (F (1,14) = 48.19; p<.01; ɳ2=.775), but no effect of CW demand was found. Conversely to HR, an interaction between the activity and CW demand was found for RR (F (1,14) = 11.55; p<.01; ɳ2=.452). Whereas the post hoc analysis showed that RR increased for HCW in comparison to LCW under ST condition (p<.01), no significant differences between LCW and HCW were found while driving. The correlation between HR and RR was r=0.44.
Regarding the estimation of the RR via ECG by comparing the outcomes with the RR from the original respiratory signals, a significant correlation of r=.86 (p<.01) was achieved. Even if an overestimation of 1.63 inspirations/min on average was evidenced (p<.01), the inaccuracy of the RR hardly yielded the 9% on average. The statistical contrast significances from the ECG-derived RR matched the ones from the original RR measures.
Conclusions
Cardiac and respiratory signals are impacted differently by the CW in driving. Therefore, even if HR is modulated by RR in a short term, HR and RR may be employed as complementary markers reflecting the mental state under different conditions. Specifically, RR could be suitable to evidence a variation of CW when driving is not required. This fact could hint the use of this measure to monitor the driver mental state in autonomic vehicles, where a continuous driving is not required, in order to predict the available cognitive resources if the user has to take over the vehicle.
Furthermore, the computation of the ECG-derived RR represents a cost-effective alternative to recover RR without needing an additional device to measure respiration. The implementation of similar algorithms to extract RR from HR collected with photoplethysmographic signals will be desirable to obtain this valuable information from wearable devices.
Acknowledgements
This work benefited from the support of the project AUTOCONDUCT ANR-16-CE22-0007 of the French National Research Agency.
References
Grassmann, M., Vlemincx, E., von Leupoldt, A., Mittelstädt, J. M., & Van den Bergh, O. (2016). Respiratory changes in response to cognitive load: A systematic review. Neural plasticity, 2016.
Moody, G. B., Mark, R. G., Zoccola, A., & Mantero, S. (1985). Derivation of respiratory signals from multi-lead ECGs. Computers in cardiology, 12(1985), 113-116.
Rahman, H., Begum, S., & Ahmed, M. U. (2015). Driver Monitoring in the Context of Autonomous Vehicle. In 13th Scandinavian Conf. on Artificial Intelligence, Halmstad, Sweden, pp. 108-117.
Keywords:
driving,
Heart Rate,
Respiration,
cognitive workload,
Operator monitoring
Conference:
2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.
Presentation Type:
Poster Presentation
Topic:
Neuroergonomics
Citation:
Hidalgo-Muñoz
AR,
Béquet
AJ,
Astier-Juvenon
M,
Pépin
G,
Fort
A,
Jallais
C,
Tattegrain
H and
Gabaude
C
(2019). Determination of cognitive workload variation in driving from ECG derived respiratory signal and heart rate.
Conference Abstract:
2nd International Neuroergonomics Conference.
doi: 10.3389/conf.fnhum.2018.227.00058
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Received:
31 Mar 2018;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Antonio R Hidalgo-Muñoz, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), TS2, Paris, France, arhidalgom@gmail.com