Abstract
In this paper, we mainly study the emotion recognition algorithm based on ECG signals, extract the correlation feature and time-frequency domain statistical feature of ECG signals, and introduce SVW, CART and KNN three classification algorithms commonly used in emotion recognition. By comparing the accuracy of emotion recognition in the application of three classification algorithms between the correlation features of ECG signals and traditional time-frequency domain features, we found that the use of correlation features of ECG signals can get a higher recognition rate, which is 16.7%∼19.7% higher than that of the traditional feature. In addition, among the three classification algorithms, KNN algorithm can get the highest emotion recognition accuracy. In order to further improve the accuracy of emotion recognition, Max-Min Ant System is combined with KNN classification algorithm in this paper to optimize the feature combination. The overall recognition rate reaches 92%, which is 16.9% higher than the accuracy of emotion recognition directly using KNN classification algorithm.
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