A New Modeling Method of Photoplethysmography Signal Based on Lognormal Basis

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• 关键词：PPG ; Lognormal basis ; Modeling ; Daily monitoring
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9864
• 期：1
• 页码：12-21
• 全文大小：1,011 KB
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  8.Li, D., Zhao, H., Li, S., Zheng, H.: A new representation of photoplethysmography signal. In: Cai, Z., Wang, C., Cheng, S., Wang, H., Gao, H. (eds.) WASA 2014. LNCS, vol. 8491, pp. 279–289. Springer, Heidelberg (2014)
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  11.Huotari, M., Vehkaoja, A., Määttä, K., et al.: Photoplethysmography and its detailed pulse waveform analysis for arterial stiffness. J. Struct. Mech. 44(4), 345–362 (2011)
  12.Kostamovaara, J.: Arterial stiffness estimation based photoplethysmographic pulse wave analysis. In: Proceedings of the Spie, pp. 73–76 (2010)
  13.Zhao, H., Dou, S.C., Li, D.Z., et al.: Mathematical modeling of pulse wave based on lognormal function. J. Northeast. Univ. Nat. Sci. 37(2), 169–173 (2016)
  14.Database of MIMIC II (2016). http://​physionet.​org/​mimic2/​mimic2&​underscore;waveform&​underscore;overview.​shtml
  
• 作者单位：Yun Luo (22)
  Wenfeng Li (22)
  Wenbi Rao (23)
  Xiuwen Fu (22)
  Lin Yang (22)
  Yu Zhang (22)
  
  22. School of Logistics Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China
  23. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, People’s Republic of China
  
• 丛书名：Internet and Distributed Computing Systems
• ISBN：978-3-319-45940-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9864

文摘

Human photoplethysmography (PPG) signal carries abundant physio-logical and pathological information of cardiovascular system, which can be used to monitor cardiovascular health in the daily life. The existing modeling methods are mainly based on Gaussian basis, which fail to conform to the long-tail features of PPG pulse waveforms. And other several existing methods based on Lognormal basis don’t work well in daily monitoring. In this paper, we proposed a new modeling method based on the long-tail Lognormal basis. Fitting calculations get an adaptive time domain by introducing the mode of the corresponding Lognormal basis and are implemented by the proposed successive-fitting solution. The simulations have proved that the proposed method has a good fitting accuracy and efficiency and is suitable for daily monitoring of cardiovascular health in body sensor networks (BSNs). Besides that, a closer relation between the cardiovascular health and the vector parameters of the Lognormal basis also can be expected.