Toward Recognizing Two Emotion States from ECG Signals

Emotion recognition based on physiological signals which can reflect peoplepsilas real emotion correctly is more robust and objective than any other ways, so it has a bright prospect of research and applications. This paper may firstly carry out the work of feature extraction for electrocardiogram (...

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Bibliographic Details
Published in2009 International Conference on Computational Intelligence and Natural Computing Vol. 1; pp. 210 - 213
Main Authors Cheng Defu, Cai Jing, Liu Guangyuan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Computational intelligence
discrete wavelet transform
Discrete wavelet transforms
ECG
Electrocardiography
Electrodes
Emotion recognition
Feature extraction
feature selection
Humans
Particle swarm optimization
Sampling methods
Support vector machines
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Summary:Emotion recognition based on physiological signals which can reflect peoplepsilas real emotion correctly is more robust and objective than any other ways, so it has a bright prospect of research and applications. This paper may firstly carry out the work of feature extraction for electrocardiogram (ECG) obtained from 391 subjects containing two emotion states (joy, sad) by the method of discrete wavelet transform (DWT). Then feature selection could be performed using the method on the combination of particle swarm optimization (PSO) and KNN classifier. Eventually, the optimal feature subset could be found and the total recognition rate reached 84.45%. Experiment and simulation results showed that it is feasible and efficiency that using PSO and KNN to recognize emotion states by physiological signals.
ISBN:9780769536453
076953645X
DOI:10.1109/CINC.2009.240