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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

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References

Zhang ,

Jiang and

Tong , Study of sentiment classification for chinese microblog based on recurrent neural network, Acta Electronica Sinica (English) 25(4) (2016), 601–607.

Huang and

Wang , Research on chinese texts sentiment classification approach based on PSO-GP, Computer Science 20(81) (2017), 1121–1134.

E.V.

Kotelnikov and

M.V.

Pletneva , Text sentiment classification based on a genetic algorithm and word and document co-clustering, Journal of Computer & Systems Sciences International 55(1) (2016), 106–114.

He ,

Li and

Yao , Sentiment classification technology based on Markov logic networks, New Review of Hypermedia & Multimedia 22(3) (2016), 243–256.

Liu ,

J.W.

Bi and

Z.P.

Fan , A method for ranking products through online reviews based on sentiment classification and interval-valued intuitionistic fuzzy TOPSIS, International Journal of Information Technology & Decision Making 16(6) (2017), 112–120.

Tang , Research on the sentiment classification of online review based on sentiment ontology and KNN algorithm, Information Studies Theory & Application 9(34) (2016), 219–227.

Jin and

Dong , Research on clinical electrocardiogram classification algorithm based on ensemble learning, Journal of Biomedical Engineering 12(5) (2016), 143–157.

Tian ,

Wu and

K.M.

Chao , A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews, Electronic Commerce Research & Applications 16(8) (2016), 66–76.

Xu and

Hua , Secured ECG signal transmission for human emotional stress classification in wireless body area networks, Eurasip Journal on Information Security 2016(1) (2016), 1–12.

10.

Chukwunonyerem ,

A.M.

Aibinu and

A.J.

Onumanyi , Development of key generation algorithm using ECG biometrics for node security in wireless body area sensor network, European Research in Telemedicine/La Recherche Européenne en Télémédecine 5(4) (2016), 137–144.

11.

S.R.

Moosavi ,

Nigussie and

Levorato , Low-latency approach for secure ECG feature based cryptographic key generation, IEEE Access PP(99) (2017), 1–1.

12.

A.S.

Abiodun ,

M.H.

Anisi and

Ali , Reducing power consumption in wireless body area networks: A novel data segregation and classification technique, IEEE Consumer Electronics Magazine 6(4) (2017), 38–47.

13.

Steffen ,

P.R.

Bhanu and

Farshad , Design of secure ECG-based biometric authentication in body area sensor networks, Sensors 16(4) (2016), 570–570.

14.

Li ,

Cai and

W.S.

Lee , A wearable biofeedback control system based body area network for freestyle swimming, Conf Proc IEEE Eng Med Biol Soc 2016 (2016), 1866–1869.

15.

Saadeh ,

M.A.B.

Altaf and

Alsuradi , A 1.1-mW ground effect-resilient body-coupled communication transceiver with pseudo OFDM for head and body area network, IEEE Journal of Solid-State Circuits 5(1) (2017), 1–13.

16.

Murugappan ,

Ramachandran and

Sazali , Classification of human emotion from EEG using discrete wavelet transform, Journal of Biomedical Science and Engineering 3(4) (2010), 390.

17.

Agrafioti ,

Hatzinakos and

A.K.

Anderson , ECG pattern analysis for emotion detection, IEEE Transactions on Affective Computing 3(1) (2012), 102–115.

18.

Vaish and

Kumari , A comparative study on machine learning algorithms in emotion state recognition using ECG, Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), 2012. Springer, New Delhi, 2014, pp. 1467–1476.

19.

S.A.

Israel ,

J.M.

Irvine and

Cheng , ECG to identify individuals, Pattern recognition 38(1) (2005), 133–142.

20.

Cai ,

Liu and

Hao , The research on emotion recognition from ECG signal, Information Technology and Computer Science, 2009 ITCS 2009 International Conference on IEEE 1 (2009), 497–500.

ECG emotional classification algorithms based on volume area network

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