脉搏传播时间与血压关系的长时记忆性分析

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 569-572.

脉搏传播时间与血压关系的长时记忆性分析

李晗^1,2, 赵海², 陈星池², 林川²

辽宁工业大学电子与信息工程学院辽宁锦州121000¹
东北大学计算机科学与工程学院沈阳 110819²

出版日期:2019-02-26 发布日期:2019-02-26
作者简介:李晗(1984－),男,博士生,讲师,主要研究方向为体域网、复杂网络、嵌入式系统等,E-mail:lih_neu@163.com;赵海(1959－),男,教授,博士生导师,主要研究方向为嵌入式技术、普适计算、复杂网络等;陈星池(1987-),男,博士生,主要研究方向为体域网;林川(1988-),男,主要研究方向为能源互联网等。
基金资助:
本文受国家自然科学基金资助项目(61101121)资助。

Long Term Memory Analysis of Relationship Between Pulse Transit Time and Blood Pressure

LI Han^1,2, ZHAO Hai², CHEN Xing-chi², LIN Chuan²

School of Electronic and Information Engineering,Liaoning University of Technology,Jinzhou,Liaoning 121000,China¹
School of Computer Science & Engineering,Northeastern University,Shenyang 110819,China²

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

摘要： 相对于柯氏音法,通过脉搏传播时间估算血压不仅更为便携,还可以实现血压的连续测量。但是因为现有研究建立的线性方程的有效时间较短,所以脉搏传播时间随血压变化的机制有待进一步的分析。文中以MIMIC数据库中的10例数据为研究对象,从长时记忆的角度,以符号化和复杂网络为主要研究手段分析了血压与脉搏传播时间的关系。对网络的度分布进行了分析,结果显示收缩压网络度分布具有幂率性,验证了收缩压脉搏波传播时间关系序列的长时记忆。对血压网络节点变化的分析显示,相对于舒张压,收缩压网络的节点数能较快达到饱和,反映了某种核心状态对血压脉搏传播时间关系的持续影响。研究结果可以为通过脉搏波传播时间更精确地无创连续测量血压提供支持。

关键词: 长时记忆性, 符号化, 复杂网络, 脉搏传播时间, 血压

Abstract: Compared with the Korotkoff sound method,estimating blood pressure via pulse transit time is more portable and can be implemented for continuous measurement.However,the effective time of the linear equation established by the existing research is short,the mechanism of pulse transit time changing with the blood pressure needs further analysis.Based on 10 groups of data in MIMIC database,the relationship between blood pressure and pulse transit time was analyzed from the perspective of long-term memory,taking symbolization and complex network as the main research means.The degree distribution of the SBP network shows power-law characteristics,thus indicating the long term me-mory of the SBP-PTT time series.The node variation of the SBP network can be faster to achieve the saturation state compared with DBP network,which reflects the continuous influence of a certain core state on the SBP-PTT relationship.The results can provide a basis for the more accurate and noninvasive continuous measurement of blood pressure through the pulse wave transit time

Key words: Blood pressure, Complex network, Long term memory, Pulse wave transit time, Symbolization

中图分类号:

TP399

李晗, 赵海, 陈星池, 林川. 脉搏传播时间与血压关系的长时记忆性分析[J]. 计算机科学, 2018, 45(11A): 569-572. https://doi.org/

LI Han, ZHAO Hai, CHEN Xing-chi, LIN Chuan. Long Term Memory Analysis of Relationship Between Pulse Transit Time and Blood Pressure[J]. Computer Science, 2018, 45(11A): 569-572. https://doi.org/

参考文献

[1]PROENÇA J,MUEHLSTEFF J,AUBERT X,et al.Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population?[C]∥2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).IEEE,2010:598-601.
[2]FUNG P,DUMONT G,RIES C,et al.Continuous noninvasive blood pressure measurement by pulse transit time[C]∥26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2004(IEMBS’04).IEEE,2004:738-741.
[3]ZHANG X Y,ZHANG Y T.The effect of local cold exposure on pulse transit time[C]∥27th Annual International Conference of the Engineering in Medicine and Biology Society,2005.IEEE-EMBS 2005.IEEE,2006:3522-3525.
[4]http://www.physionet.org.
[5]CHURCHLAND P S,SEJNOWSKI T J.The computational brain[M].MIT press,2016.
[6]HURST H E.Long-term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineers,1951,116:770-808.
[7]FENG Y,LU B,ZHANG D.Multifractal manifold for rotating machinery fault diagnosis based on detrended fluctuation analysis[J].Journal of Vibroengineering,2016,18(8):5153-5173.
[8]WATTS D J,STROGATZ S H.Collective dynamics of ‘small-world’ networks [J].Nature,1998,393:440-442.
[9]BARABASI A L,ALBERT R.Emergence of Scaling in Random Networks[J].Science,1999,286:509-512.
[10]ZHANG J,MALL M.Complex Network from Pseudoperiodic Time Series:Topology versus Dynamics[J].Physical Review Letters,2006,96(23):238701.
[11]YANG Y,WANG J B,YANG H,et al.Visibility Graph Approach to Exchange Rate Series[J].Physica A,2009,388:4431-4437.
[12]SHAO Z.Network Analysis of Human Heartbeat Dynamics[J].Applied Physics Letters,2010,96:073703.
[13]KARIMI S,DAROONEH A H.Measuring persistence in a stationary time series using the complex network theory[J].Physica A:Statistical Mechanics and its Applications,2013,392(1):287-293.
[14]向馗,蒋静坪.时间序列的符号化方法研究[J].模式识别与人工智能,2007,20(2):154-161.
[15]PODOBNIK B,STANLEY H E.Detrended cross-correlation analysis:a new method for analyzing two nonstationary time series[J].Physical Review Letters,2008,100(8):084102.
[16]TSUJI H,VENDITTI F J,MANDERS E S,et al.Reduced heart rate variability and mortality risk in an elderly cohort.The Framingham Heart Study[J].Circulation,1994,90(2):878-883.
[17]BODE M,BURRAGE K,POSSINGHAM H P.Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns[J].Ecological Modelling,2008,214(2):201-209.

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