Impact of Variable Autonomous Neural Gain to Cardiovascular System Control

被引:0
|
作者
Kozelek, P. [1 ]
Holcik, J. [1 ]
机构
[1] Czech Tech Univ, Fac Biomed Engn, Kladno 27201, Czech Republic
关键词
simulation; autonomous cardiovascular system control; modeling;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Time responses of some equine ECG signal parameters after stimulation appear rather non-standard when compared with the responses known in human electrocardiology. Heart rate of the stimulated equine cardiovascular system accelerates; it means the RR intervals describing lengths of cardiac cycles shorten, as is the case in human ECG. However, there exist a variety of different responses in equine QT intervals. We distinguish three different responses of QT intervals representing electrical activity of myocardial ventricles. First, shortening of the QT intervals (the same as in human population, 30% of records); second, prolonging of QT intervals (inverse relationship, 33% of records) and third, prolonging QT consequently followed by its shortening (complex relationship, 22%); 15% of responses could not be classified. Several mathematical models were designed in the past to explain the phenomenon based on an open loop control by means of sympathetic and vagal branches of the autonomous neural system. The models were not very specific in explaining the background of the complex relationship between RR and QT intervals in equine ECG. That is why another parameters defining variable gain of both the autonomous neural branches were used in the developed models. Simulation results with the modified model have shown that it is possible to explain all the above mentioned QT responses with mutual balance between the gain functions. The slope of the function describing dependency of a total neural sensitivity on the heart rate seems to be the most significant parameter to characterize the QT response: negative slope being a sign of prolonging QT sequences and vice versa.
引用
收藏
页码:3432 / 3435
页数:4
相关论文
共 50 条
  • [21] Neural network inverse control of speed variable system for BLDCM
    Liu, Guohai
    Jin, Peng
    Wei, Haifeng
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2010, 25 (08): : 24 - 30
  • [22] The Reserch of Variable Structure Fuzzy Neural Network Control system
    Qiu Huanyao
    2016 IEEE INTERNATIONAL CONFERENCE OF ONLINE ANALYSIS AND COMPUTING SCIENCE (ICOACS), 2016, : 273 - 276
  • [23] Variable Neural Adaptive Robust Control: A Switched System Approach
    Lian, Jianming
    Hu, Jianghai
    Zak, Stanislaw H.
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (05) : 903 - 915
  • [24] Variable gain for iterative learning control
    Su J.
    Zhang Y.
    Chen M.
    Recent Advances in Computer Science and Communications, 2021, 14 (03): : 788 - 792
  • [25] Research on Process of Impact and Control with Underwater Autonomous Weapon System
    Peng, Pengfei
    Liu, Zhong
    Jiang, Jun
    ADVANCED RESEARCH ON INDUSTRY, INFORMATION SYSTEMS AND MATERIAL ENGINEERING, PTS 1-7, 2011, 204-210 : 862 - 865
  • [26] Study on control of planar variable geometry truss used for autonomous docking system
    Senda, Kei
    Murotsu, Yoshisada
    Mitsuya, Akira
    Kawano, Hidefumi
    Ando, Akihiro
    Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 1997, 2 : 1449 - 1455
  • [27] Neural network compensation for force tracking control of an autonomous helicopter system
    Eom, Il Yong
    Jung, Seul
    2007 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS, VOLS 1-6, 2007, : 2145 - 2150
  • [28] Autonomous flight control system for unmanned helicopter using neural networks
    Nakanishi, H
    Hashimoto, H
    Hosokawa, N
    Sato, A
    Inoue, K
    SICE 2002: PROCEEDINGS OF THE 41ST SICE ANNUAL CONFERENCE, VOLS 1-5, 2002, : 777 - 782
  • [29] Stochastic controllability of a non-autonomous impulsive system with variable delays in control
    Khatoon, Areefa
    Raheem, Abdur
    Afreen, Asma
    FILOMAT, 2023, 37 (24) : 8175 - 8191
  • [30] Multi-channel variable structure system for the control of autonomous underwater vehicle
    Lebedev, Alexander
    Filaretov, Vladimir
    2007 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS I-V, CONFERENCE PROCEEDINGS, 2007, : 221 - 226