Feature Extraction, Feature Selection and Classification from Electrocardiography to Emotions

Electrocardiography (ECG) is one of the most important physiological signals, whose changes can reflect the changes in emotional states in some degree. Raw ECG data were recorded when film clips were used to elicit target emotions (joy and sadness) of multiple subjects. Wavelet transform was applied...

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Bibliographic Details
Published in2009 International Conference on Computational Intelligence and Natural Computing : 6-7 June 2009 Vol. 1; pp. 190 - 193
Main Authors Ma, Chang-wei, Liu, Guang-yuan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
ACS
Cameras
Data acquisition
Data mining
ECG
Electrocardiography
Emotion recognition
Feature extraction
feature selection
Support vector machine classification
Support vector machines
Testing
wavelet transform
Wavelet transforms
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Summary:Electrocardiography (ECG) is one of the most important physiological signals, whose changes can reflect the changes in emotional states in some degree. Raw ECG data were recorded when film clips were used to elicit target emotions (joy and sadness) of multiple subjects. Wavelet transform was applied to accurately detect QRS complex for its advantages on time-frequency localization, in order to extract features from raw ECG signals. A method of feature selection based on Ant Colony System (ACS), using K-nearest neighbor for emotion classification, was introduced to obtain higher recognition rate and effective feature subset.
ISBN:9780769536453
076953645X
DOI:10.1109/CINC.2009.126