Preprocessing of PPG and EDA signals for detection of emotional and cognitive states via physiological signals

  • Kalin Kalinkov Technical University of Varna, Varna Bulgaria, Department of Communication Engineering and Technologies
  • Valentina Markova Technical University of Varna, Varna Bulgaria, Department of Communication Engineering and Technologies http://orcid.org/0000-0002-1819-4507
Keywords: PPG, EDA, cognitive, preprocessing, emotions

Abstract

Presented in the current paper is a methodology for approaching the preprocessing of Photoplethysmography and Electrodermal activity for the detection of emotional and cognitive states in humans via physiological signals. Examined closely are the effects of downsampling and segmentation of the PPG, the segmentation and separation of the Skin Conductance Level (SCL), and Skin Conductance Response (SCR) components of the EDA signal with both median and low pass filters. The results from the research indicate that the most appropriate preprocessing with regard to emotions and cognitive load classification is segmentation of 2 minutes which is the recommended length for frequency analysis of heart rate variability. Recommended, furthermore, is the downsampling of the PPG to 64 Hz, which proved to be the lowest sampling frequency that doesn’t introduce errors in the systolic peak detection, neither does it drastically affect the length of the Inter Beat Intervals (IBIs). Proposed, as to the separation of the SCL component of the EDA, is the usage of median filter with window length of 75% of the sampling frequency, which introduces negligible artefacts, mainly at the start of the signal.

References

Yang, X., McCoy, E., Anaya-Boig, E., Avila-Palancia, I., Brand, C., Carrasco-Turigas, G., Dons, E., Gerike, R., Goetschi, T., Niuewenhuijsen, M., Pablo Orjuela, J. and Int Panis, L. (2021). The effects of traveling in different transport modes on galvanic skin response (GSR) as a measure of stress: An observational study. Environment International, 156, 106764.
Crossref

Bradke, B. S., Miller, T. A., & Everman, B. (2021). Photoplethysmography behind the ear outperforms electrocardiogram for cardiovascular monitoring in dynamic environments. Sensors, 21(13), 4543.
Crossref

Koelstra, S., Muhl, C., Soleymani, M., Jong-Seok Lee, Yazdani, A., Ebrahimi, T., … Patras, I. (2012). DEAP: A Database for Emotion Analysis Using Physiological Signals. IEEE Transactions on Affective Computing, 3(1), 18–31.
Crossref

Sharma, K., Castellini, C., van den Broek, E. L., Albu-Schaeffer, A., & Schwenker, F. (2019). A dataset of continuous affect annotations and physiological signals for emotion analysis. Scientific Data, 6(1), 1-13.
Crossref

Markova, V., Ganchev, T., & Kalinkov, K. (2019). CLAS: A database for cognitive load, affect and stress recognition. Paper presented at the Proceedings of the International Conference on Biomedical​​​​​ Innovations and Applications, BIA 2019.  ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​
Crossref

Li, W., Yang, C., & Fang, W. (2020). A real-time emotion recognition system based on an AI system-on-chip design. Paper presented at the Proceedings - International SoC Design Conference, ISOCC 2020, 29-30.
Crossref

Geršak, G. (2020). Electrodermal activity - A beginner’s guide. Elektrotehniski Vestnik/Electrotechnical Review, 87(4), 175-182.
Retrieved from www.scopus.com

Ganapathy, N., Veeranki, Y. R., Kumar, H., & Swaminathan, R. (2021). Emotion recognition using electrodermal activity signals and multiscale deep convolutional neural network. Journal of Medical Systems, 45(4), 1-10.
Crossref

Morresi, N., Casaccia, S., & Revel, G. M. (2021). Metrological characterization and signal processing of a wearable sensor for the measurement of heart rate variability. Paper presented at the 2021 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2021 - Conference Proceedings.
Crossref

Kalinkov, K., Markova, V., & Ganchev, T. (2020). Heart rate variability calculation methods. Paper presented at the Proceedings of the International Conference on Biomedical Innovations and Applications, BIA 2020, 97-100.
Crossref

Béres, S., & Hejjel, L. (2021). The minimal sampling frequency of the photoplethysmogram for accurate pulse rate variability parameters in healthy volunteers. Biomedical Signal Processing and Control, 68, 102589.
Crossref

Cosoli, G., Poli, A., Scalise, L., & Spinsante, S. (2021). Heart rate variability analysis with wearable devices: Influence of artifact correction method on classification accuracy for emotion recognition. Paper presented at the Conference Record - IEEE Instrumentation and Measurement Technology Conference, 2021-May.
Crossref


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Published
2022-11-16
How to Cite
Kalinkov, K., & Markova, V. (2022, November 16). Preprocessing of PPG and EDA signals for detection of emotional and cognitive states via physiological signals. ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA, 6(2), 49-56. https://doi.org/10.29114/ajtuv.vol6.iss2.253
Section
INFORMATION TECHNOLOGIES, COMMUNICATION AND COMPUTER EQUIPMENT
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