A Comparative Study on Machine Learning Algorithms in Emotion State Recognition Using ECG

Human-Computer-Interface (HCI) has become an emerging area of research among the scientific community. The uses of machine learning algorithms are dominating the subject of data mining, to achieve the optimized result in various areas. One such area is related with emotional state classification using bio-electrical signals. The aim of the paper is to investigate the efficacy, efficiency and computational loads of different algorithms scientific comparisons that are used in recognizing emotional state through cardiovascular physiological signals. In this paper, we have used Decision tables, Neural network, C4.5 and Naive Bayes as a subject under study, the classification is done into two domains: High Arousal and Low Arousal.