Abstract
This study’s aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.
Highlights
Emotion recognition by using physiological signals, including electroencephalogram (EEG) [1], photoplethysmography (PPG) [2], skin temperature [3], and electrocardiogram (ECG) [4], has attracted increasing attention
permutation min-entropy (PME) appears be more sensitive of fortheir andrandomness
Previous studies showing that other associated RR interval (RRI) time series
Summary
Emotion recognition by using physiological signals, including electroencephalogram (EEG) [1], photoplethysmography (PPG) [2], skin temperature [3], and electrocardiogram (ECG) [4], has attracted increasing attention. Time-domain and frequency-domain indices were conventionally used for emotional state recognition. Guo et al [5] applied time-domain indices, mean, coefficient of variation of RR intervals, standard deviation of the RR intervals, and standard deviation of the successive differences of the RR intervals together with a support vector machine classifier, reaching 50.3% of correct rate to discriminate negative and positive emotional states. Kim et al [6] used spectrum, amplitude, mean, maximum, and standard deviation together with a support vector machine classifier to achieve an accuracy of 61.8%
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