Year 2020, Volume 8 , Issue 2, Pages 407 - 414 2020-05-26

Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition
Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition

Ramis İLERİ [1] , Fatma LATİFOGLU [2]


Bu çalışmada, fiziksel stres, bilişsel stres ve duygusal stres arasındaki değişiklikleri değerlendirmek için Elektrodermal Aktivite (EDA) sinyalleri analiz edilmiştir. Bu amaçla, EDA sinyallerinin zaman ekseninde enerji ve varyans özellikleri, her bir durum için analiz edilmiştir. Ek olarak, EDA sinyalleri, Ampirik Kip Ayrışımı (AKA) yöntemi kullanılarak alt bandlara ayrıştırımış ve her bir stres durumu için alt bant sinyalleri analiz edilmiştir. Kısa Zamanlı Fourier Dönüşümü (STFT) yöntemi, bu sinyallerin zaman-frekans alanında analiz etmek için kullanılmıştır. Ayrıca, elde edilen özelliklere göre, farklı stres durumlarını belirlemek için EDA sinyalleri sınıflandırılmıştır. Elde edilen sonuçlar, EDA ve alt bant EDA sinyallerinin bilişsel stres açısından diğer stres durumlarından anlamlı derecede farklı olduğunu göstermiştir. Ayrıca, AKA alt bantlarından elde edilen özellikler, farklı durumlar için Destek Vektör Makineleri (DVM), K-En Yakın Komşular (KNN) ve Çok Katmanlı Algılayıcı (ÇKA) yöntemleri kullanılarak sınıflandırılmış ve sınıflandırıcı performansları karşılaştırılmıştır. Diğer durumlarla karşılaştırıldığında, bilişsel stres durumu ve ilk dinlenme periyodunun sınıflandırılmasında, en iyi sınıflandırma performansı MLP, SVM ve KNN sınıflandırıcısı kullanılarak sırasıyla % 97,36, % 84,21 ve % 81,57 olarak elde edilmiştir.

In this study, Electrodermal Activity (EDA) signals were analyzed to evaluate the changes between physical stress, cognitive stress, and emotional stress. For this purpose, energy and variance properties of the EDA signals in the time domain were analyzed for each case and as short-time frames. In addition, the EDA signals were decomposed using the Empirical Mode Decomposition (EMD) method, and the sub-band signals were analyzed for each case. Further, the Short Time Fourier Transform (STFT) method was used to analyze the in the time-frequency domain of these signals. Also, according to obtained features, EDA signals were classified to determine the stages. Simulated results show that, the EDA and subband EDA signals were found to be significantly different in terms of cognitive stress (p<0.05). Also, the features obtained from the EMD subbands were classified using Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) methods for different situations and classifier performances were compared. In the classification of cognitive stress period and first rest period, the best classification performance was achieved as 97.36 %, 84,21 %, and 81,57 % using MLP, SVM and KNN classifier respectively compared to other situations.

  • [1] Bouscein W., Electrodermal Activity. ,Newyork, Plenum Pres., pp 1-372, 1992.
  • [2] Dolu N, Özbek H, Sporcularda Dikkat Düzeyindeki Hemisferik Farklılıkların Elektrodermal Aktivite İle Belirlenmesi, Erciyes Üniversitesi, Kayseri, 2009
  • [3] Y. J. Kim, G. R. Jeon, S. S. Kim, W. Y. Jang, J. H. Kim, and S. W. Baik, “Implementation of Electrodermal Activity Measurement System using Algometer and Biopotential Measuring System”, International Conference on Chemistry, Antalya, 2014
  • [4] Dolu N., “Sağlıklı Kişilerde ve Hipertiroidili Hastalarda Elektrodermal Aktivite Bulgularının İncelenmesi”, Uzmanlık Tezi, Erciyes Üniversitesi
  • [5] Tarvainen, M.P., Karjalainen, P. A., Koistinen, A.S., Valkonen- Korhonen, M., 2000, “ Principal component analysis of galvanic skin responses “, Proceedingsof the 22" d Annual EMBS International Conference of the IEEE Engineering in Medicine and Biology Society Vols 1-4, Vol.22, July 23-28, Chicago IL., pp.3011- 3014
  • [6] Xiong, J., 2010, “ Design of Health Relaxation System based on Biofeedback from Finger Sensors”, Innovative Computing & Communication, 2010 Intl Conf on and Information Technology & Ocean Engineering, 2010 Asia- Pacific Conf on (CICC-ITOE) , pp. 127-128
  • [7] Bouscein W., Electrodermal Activity, Springer Science & Business Media, 2012
  • [8] S. Zhang, S. Hu, H.H. Chao, X. Luo, O.M. Farr, C.-s.R. Li, Cerebral correlates of skin conductance responses in a cognitive task, NeuroImage, vol 62, pp 1489-1498, 2012
  • [9] MM. Bradley, P.J. Lang, Motivation and Emotion, in Handbook of Psychophysiology, 2nd edition, Cambridge University Press, New York, pp. 581-607.,2006.
  • [10] C. Setz, B. Arnrich, J. Schumm, R. La Marca, G. Tröster, and U. Ehlert, "Discriminating stress from cognitive load using a wearable EDA device.," IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society, vol. 14, no. 2, pp. 410-7, Mar. 2010
  • [11] Rabavilas AD. Electrodermal activity in low and high alexithymic neurotic patients. Psychother Psychosom, vol 4, pp 47:101, 2006.
  • [12] J.H Satterfield, M.E Dawson Electrodermal correlates of hyperactivity in children Psychophysiology, vol 8,pp. 191-197 , 1971
  • [13] J. A. Healey and R. W. Picard, “Detecting stress during real-world driving tasks using physiological sensors,” IEEE Transactions in Intelligent Transportation Systems, vol. 6(2), pp. 156-166, 2005
  • [14] Birjandtalab, Javad, Diana Cogan, Maziyar Baran Pouyan, and Mehrdad Nourani, A Non-EEG Biosignals Dataset for Assessment and Visualization of Neurological Status, 2016 IEEE International Workshop on Signal Processing Systems (SiPS), Dallas, TX, pp. 110-114., 2016. doi: 10.1109/SiPS.2016.27
  • [15] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215]; 2000 (June 13).
  • [16] Konstantin Dragomiretskiy, Dominique Zosso,Variational Mode Decomposition, IEEE Transactions on Signal Processing, Vol 62, No 3, pp. 531-544, February 2014.
  • [17] Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, Henry H. LiuProc. R. Soc. Lond. A. "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis." Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol 454, pp 903-995, 1998. Doi: 10.1098/rspa.1998.0193.
  • [18] Huang, N.E., Shen, Z. and Long, S.R, A new view of nonlinear water waves:The Hilbert Spectrum. Annu. Rev. Fluid Mech.,vol 454, pp 903–995,1999.
  • [19] Huang, N.E., Wu, M.L., Long, S.R., Shen, S., Qu, W., Gloerson, P. and Fan, K. (2003) A confidence limit for the empirical mode decomposition and hilbert spectral analysis. Proc. Roy. Soc. London A., 31, 417–457
  • [20] Rilling, G & Flandrin, Patrick & Gonçalves, Paulo. "On empirical mode decomposition and its algorithms." IEEE-EURASIP workshop on nonlinear signal and image processing 2003. NSIP-03. Grado, Italy. 8-11.
  • [21] Erkaymaz, H., Özer, M. ve Orak, İ. M.,Detection of Directional Eye Movements Based on The Electrooculogram Signals Through an Artificial Neural Network, Chaos Solitons&Fractals, vol 56 ,pp 202- 208, 2015.
  • [22] Charu C. Aggarwal,Neural Networks and Deep Learning : A Textbook,Cham, Switzerland, 2018
  • [23] [25] https://towardsdatascience.com/knn-k-nearest-neighbors-1-a4707b24bd1d , Access date : 19.07.2019
  • [24] Cortes, C. , & Vapnik, V. ,Support-vector networks. Machine Learning, vol 20 (3),pp 273–2, 1995
  • [25] P. Ghaderyan, A. Abbasi, An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations, Int. J. Psychophysiol. Vol 110, pp 91-101, 2016.
  • [26] J. Kim, E. André, Emotion recognition based on physiological changes in music listening, IEEE Trans. Pattern Anal. Mach. Intell. Vol 30 (12) , pp 2067-2083, 2008.
  • [27] W. Wen, G. Liu, N. Cheng, J. Wei, P. Shangguan, W. Huang, Emotion recognition based on multi-variant correlation of physiological signals, IEEE Trans. Affect. comput. vol 5 (2) ,pp 126-140, 2014.
Primary Language en
Subjects Engineering
Published Date Mayıs 2020
Journal Section Articles
Authors

Orcid: 0000-0001-8128-118X
Author: Ramis İLERİ (Primary Author)
Institution: ERCIYES UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF BIOMEDICAL ENGINEERING
Country: Turkey


Orcid: 0000-0003-2018-9616
Author: Fatma LATİFOGLU
Institution: ERCIYES UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF BIOMEDICAL ENGINEERING
Country: Turkey


Dates

Publication Date : May 26, 2020

Bibtex @research article { apjes601235, journal = {Akademik Platform Mühendislik ve Fen Bilimleri Dergisi}, issn = {}, eissn = {2147-4575}, address = {}, publisher = {Academic Platform}, year = {2020}, volume = {8}, pages = {407 - 414}, doi = {10.21541/apjes.601235}, title = {Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition}, key = {cite}, author = {İleri̇, Ramis and Lati̇foglu, Fatma} }
APA İleri̇, R , Lati̇foglu, F . (2020). Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition . Akademik Platform Mühendislik ve Fen Bilimleri Dergisi , 8 (2) , 407-414 . DOI: 10.21541/apjes.601235
MLA İleri̇, R , Lati̇foglu, F . "Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition" . Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 (2020 ): 407-414 <https://dergipark.org.tr/en/pub/apjes/issue/52467/601235>
Chicago İleri̇, R , Lati̇foglu, F . "Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 (2020 ): 407-414
RIS TY - JOUR T1 - Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition AU - Ramis İleri̇ , Fatma Lati̇foglu Y1 - 2020 PY - 2020 N1 - doi: 10.21541/apjes.601235 DO - 10.21541/apjes.601235 T2 - Akademik Platform Mühendislik ve Fen Bilimleri Dergisi JF - Journal JO - JOR SP - 407 EP - 414 VL - 8 IS - 2 SN - -2147-4575 M3 - doi: 10.21541/apjes.601235 UR - https://doi.org/10.21541/apjes.601235 Y2 - 2020 ER -
EndNote %0 Akademik Platform Mühendislik ve Fen Bilimleri Dergisi Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition %A Ramis İleri̇ , Fatma Lati̇foglu %T Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition %D 2020 %J Akademik Platform Mühendislik ve Fen Bilimleri Dergisi %P -2147-4575 %V 8 %N 2 %R doi: 10.21541/apjes.601235 %U 10.21541/apjes.601235
ISNAD İleri̇, Ramis , Lati̇foglu, Fatma . "Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 / 2 (May 2020): 407-414 . https://doi.org/10.21541/apjes.601235
AMA İleri̇ R , Lati̇foglu F . Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition. APJES. 2020; 8(2): 407-414.
Vancouver İleri̇ R , Lati̇foglu F . Analysis of The Electrodermal Activity Signals for Different Stressors Using Empirical Mode Decomposition. Akademik Platform Mühendislik ve Fen Bilimleri Dergisi. 2020; 8(2): 407-414.