Research on emotion recognition based on image features of ECG signal and convolutional neural network
In order to improve the accuracy of emotion recognition,we used convolutional neural network and transfer learning method to propose an emotion recognition method based on electrocardiography(ECG)signal image features.First,the ECG signal was preprocessed to remove noise,and then the time-domain waveform and time-frequency graph of the ECG signal were extracted.Final-ly,transfer learning and the time-domain and frequency-domain features contained in the dual input EfficientNetV2 network learning images were used and classified to obtain the corresponding emotion categories.The results of validation on the public dataset Amigos showed that the recognition accuracy of arousal,titer and dominance were 91.63%,95.27%and 92.32%,respectively.Compared to other emotion recognition methods,this method has higher accuracy.
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