[Purpose] Intelligent emotion assessment systems have been highly successful in a
variety of applications, such as e-learning, psychology, and psycho-physiology. This study
aimed to assess five different human emotions (happiness, disgust, fear, sadness, and
neutral) using heart rate variability (HRV) signals derived from an electrocardiogram
(ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean
age of 23 years participated in this experiment. [Methods] All five emotions were induced
by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and
were preprocessed using a Butterworth 3rd order filter to remove noise and baseline
wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete
wavelet transform (DWT) was used to extract statistical features from the HRV signals
using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and
Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were
used to map the statistical features into corresponding emotions. [Results] KNN provided
the maximum average emotion classification rate compared to LDA for five emotions (sadness
− 50.28%; happiness − 79.03%; fear − 77.78%; disgust − 88.69%; and neutral − 78.34%).
[Conclusion] The results of this study indicate that HRV may be a reliable indicator of
changes in the emotional state of subjects and provides an approach to the development of
a real-time emotion assessment system with a higher reliability than other systems.
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