Abstract：Objective To improve the accuracy and efficiency of emotion recognition by using characteristic parameters of ECG signals, a genetic algorithm called improved kernel principal component analysisgenetic algorithmback propagation (IKPCA-GA-BP) neural network based on improved kernel principal component analysis is proposed. Methods First, taking the data recorded by ECG sensors as test samples, the improved kernel principal component analysis algorithm with adaptive selection of γ values was used to perform data dimension reduction and data reconstruction on multiple groups of features, which had been extracted by the binary spline wavelet transform, to obtain comprehensive variables. Secondly, the back propagation neural network model was established, and the genetic algorithm was used to optimize the initial weights and bias values of the network. Finally, by changing the proportion of model training samples to test samples, the effects of emotion classification separately based on IKPCA-GA-BP algorithm and traditional recognition algorithm were comparatively analyzed. ResultsBy this algorithm, related emotions could be recognized in about 1 s on the premise of an ensured accuracy rate of 96%. In addition, most models did not perform well in identifying sadness emotion, however, IKPCAGABP algorithm obtained an accuracy rate of nearly 100%. Conclusion In ECG signals, Pwave, QRS complex and T-wave contain a lot of information that is helpful for emotion recognition (for example, R-R interval, P-wave amplitude, etc.). However, this information can not be directly used for experimental analysis, and requires effective combination and processing to maximize its efficacy. Furthermore, among four emotions: happiness, relaxation, sadness and fear, accurate recognition of sadness often challenges most recognition algorithms.
