Abstract
Complex fluctuations of heart rate variability (HRV) reflect the autonomic regulation of the heart. At present,the characteristics of different HRV signals are not obvious and the classification accuracy is not high which restricts their practical use. Therefore, this paper proposes a signal pattern recognition algorithm based on KNN classification for HRV signal analysis and recognition. HRV characteristic matrix is formed by HRV characteristic values in frequency domain which is extracted by AR spectrum analysis, time domain characteristic values and nonlinear characteristic values. Then the matrix is processed by the K-nearest neighbor classification algorithm. In order to verify the accuracy of the algorithm, four types of ECG data in MIT-BIH database are used to train and detect the algorithm in this paper. The proposed algorithm’s accuracy of HRV classification and recognition can be up to 93.5%, which is higher than other classification recognition algorithms of the same type. These results provide a new method for extraction and classification the eigenvalues of heart rate variability.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hon, E.H., Lee, S.T.: Electronic evaluation of the fetal heart rate. Patterns preceding fetal death, further observations. Am. J. Obset. Gynecol. X 87, 814–826 (1965)
Liu, X., Miao, D., et al.: Study on heart rate variability of normal young men under different stress situations. Chin. Behav. Med. Sci. 11(6), 679–680 (2002). (in Chinese)
Shao, S., Wang, T., Song, C., et al.: A new measure of heart rate variability. J. Phys. 68(17), 1–12 (2019). (In Chinese)
Pan, Z.: Random Signals and Systems, pp. 23–56. National Defense Industry Press, Beijing (2013). (in Chinese)
Li, F.: Study on ECG signal processing based on wavelet transform. Master’s thesis of Nanjing University of Aeronautics and Astronautics (2009). (in Chinese)
Rao, J.: Study and Application of Heart Rate Variability Analysis Method. South China University of Technology, Guangzhou (2016). (in Chinese)
Chai, X., Wang, B., et al.: Study on the method to determine the optimal order in the analysis of heart rate variability autoregressive model. J. Biomed. Eng. 32(5), 958–964 (2015). (in Chinese)
Wang, Y.: Study on heart failure diagnosis model based on heart rate variability. Master’s thesis of Shandong University (2018). (in Chinese)
Yu, S.: Morphological recognition of ECG waveform. Master’s thesis of Zhejiang Normal University (2014). (in Chinese)
Khorrami, H., Moavenian, M.: A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Syst. Appl. 37(8), 5751–5757 (2010)
Lisukor, M.: A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification. Expert Syst. Appl. 37(4), 3088–3093 (2010)
Acknowledgments
This research was supported by innovation team Science foundation of Engineering University of PAP (KYTD201905).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, J., Zhuang, H., Lu, L. (2020). Heart Rate Variability Analysis Method Based on KNN Classification. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-030-43306-2_112
Download citation
DOI: https://doi.org/10.1007/978-3-030-43306-2_112
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43305-5
Online ISBN: 978-3-030-43306-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)