Affiliations: [a] School of Measurement-Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, China
| [b]
School of Software and Microelectronics, Harbin University of Science and Technology, Harbin, Heilongjiang, China
Correspondence:
[*]
Corresponding author. Zhang Zhu, School of Measurement-Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, 150080, China. E-mail: zhangzhu@hrbust.edu.cn.
Abstract: The transmission of ECG signal is a key technology of wireless body area network technology center. An ECG emotion classification algorithm based on body area network is proposed. In this method, SV vector function is used to fit ECG signals, and the fitting parameters are obtained. After estimating the channel characteristics, the fixed-point parameters are transmitted. Firstly, the wireless body area network technology is analyzed, because the body area network can deal with long-distance dependence and capture the semantic information of input text. Wireless body area network is used to extract the grammatical features of input text. Then, based on the wireless field of network technology, the principle of support vector machine (SVM) is proposed. On the basis of the emotion classification model, an algorithm based on speech recognition is constructed, and the input text vector obtained by CNN is used to represent the emotion category of the output layer. Finally, the experimental results show that the algorithm is effective, and the emotional classification model can obtain the highest accuracy in multiple data sets. The results show that the algorithm can not only fit the waveform of ECG emotional signals well, reduce the compression ratio and achieve a certain fitting effect, but also improve the detection and transmission ability of ECG (emotional) ECG signals.
Keywords: Body area network, ECG, emotion classification algorithm
