Electrodermal activity (EDA) reflects sympathetic nervous system activity

through sweating-related changes in skin conductance. Decomposition analysis

is used to deconvolve the EDA into slow and fast varying tonic and phasic

activity, respectively. In this study, we used machine learning models to compare

the performance of two EDA decomposition algorithms to detect emotions such as

amusing, boring, relaxing, and scary. The EDA data considered in this study were

obtained from the publicly available Continuously Annotated Signals of Emotion

(CASE) dataset. Initially, we pre-processed and deconvolved the EDA data into

tonic and phasic components using decomposition methods such as cvxEDA and

BayesianEDA. Further, twelve-time domain features were extracted from the phasic

component of EDA data. Finally, we applied machine learning techniques such

as logistic regression (LR) and support vector machine (SVM), to evaluate the performance

of the decomposition method. Our results imply that the BayesianEDA

decomposition method outperforms the cvxEDA. The mean of first derivative feature

discriminated all the considered emotional pairs with high statistical significance

(p<0.05). SVM was able to detect emotions better than the LR classifier.

We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision,

and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively,

using BayesianEDA and SVM classifier. The proposed framework can be utilized

to detect emotional states for the early diagnosis of psychological conditions.