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

Abstract

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.

Keywords

• Electrodermal Activity (EDA),Empirical Mode Decomposition (EMD),Short Time Fourier Transform (STFT),Artifical Neural Network (ANN)

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

Abstract

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.

Keywords

• Elektrodermal Aktivite (EDA),Ampirik Kip Ayrışımı (AKA),Kısa Zamanlı Fourier Dönüşümü (KZFD),Yapay Sinir Ağları(YSA)

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