Open AccessDIFFERENTIATION OF DICHOTOMOUS EMOTIONAL STATES IN ELECTRODERMAL ACTIVITY SIGNALS USING HIGHER-ORDER CROSSING FEATURES AND PARAMETRIC CLASSIFIERS
Author(s) -
Yedukondala Rao Veeranki,
Nagarajan Ganapathy,
Ramakrishnan Swaminathan
Publication year - 2021
Publication title -
biomedical sciences instrumentation
Language(s) - English
Resource type - Journals
ISSN - 1938-1158
DOI - 10.34107/yhpn9422.04322
Subject(s) - naive bayes classifier , artificial intelligence , pattern recognition (psychology) , linear discriminant analysis , multilayer perceptron , parametric statistics , classifier (uml) , psychology , feature extraction , perceptron , computer science , logistic regression , machine learning , speech recognition , support vector machine , mathematics , artificial neural network , statistics
Prediction and recognition of happy and sad emotional states play important roles in many aspects of human life. In this work, an attempt has been made to classify them using Electrodermal Activity (EDA). For this, EDA signals are obtained from a public database and decomposed into tonic and phasic components. Features, namely Hjorth and higher-order crossing, are extracted from the phasic component of the signal. Further, these extracted features are fed to four parametric classifiers, namely, linear discriminant analysis, logistic regression, multilayer perceptron, and naive bayes for the classification. The results show that the proposed approach can classify the dichotomous happy and sad emotional states. The multilayer perceptron classifier is accurate (85.7%) in classifying happy and sad emotional states. The proposed method is robust in handling the dynamic variation of EDA signals for happy and sad emotional states. Thus, it appears that the proposed method could be able to understand the neurological, psychiatrical, and biobehavioural mechanisms of happy and sad emotional states.