基于心电信号图像特征及卷积神经网络的情绪识别研究
Research on emotion recognition based on image features of ECG signal and convolutional neural network
李永康 1方安成 1陈娅南 1谢子奇 1潘帆 1何培宇1
作者信息
- 1. 四川大学 电子信息学院,成都 610065
- 折叠
摘要
为提高情绪识别的准确率,本研究利用卷积神经网络和迁移学习,提出了一种基于心电(electrocardiography,ECG)信号图像特征的情绪识别方法.首先对ECG信号进行预处理,去除噪声;然后提取ECG信号的时域波形图和时频图;最后,利用迁移学习和双输入EfficientNetV2 网络学习图像的时域和频域特征并进行分类,得到对应的情绪类别.在公开数据集Amigos上进行验证,结果显示,本研究在唤醒度、效价和优势度的识别准确率分别为 91.63%,95.27%和 92.32%.相较于其它情绪识别方法,本研究方法具有更高的准确率.
Abstract
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.
关键词
情绪识别/心电信号/特征提取/双输入/卷积神经网络Key words
Emotion recognition/Electrocardiography/Feature extraction/Dual input/Convolutional neural network引用本文复制引用
基金项目
四川省自然科学基金(2022NSFSC0799)
出版年
2024
国家科技期刊平台
NSTL
万方数据