Emotional quality level recognition based on HRV

This paper explores the detection of emotional levels, high, medium and low from ECG signals. Features of ECG are extracted from frequency domain, time domain and nonlinear method and normalized by z-score method. Then SFS feature selection is applied followed by LDA feature transformation. After that, the transformed features are applied to KNNR classifier. According to our results, it was found that subjects of different characteristics reveal different biosignal responses. While subjects are regarded as optimistic characteristics, they have higher responses on positive films. On the other hand, the pessimistic subjects have higher responses on negative films. When classifiers are established separately for optimistic and pessimistic subjects, we can achieve the recognition rate of 97.8%, where 7 features are selected, and 94.0%, where 8 features are selected, for optimistic and pessimistic groups respectively. When the classification is built from all the subjects, the recognition rate is reduced slightly, but it can still maintain a recognition rate of 90.4%.