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
  • [1] Autonomous Thermal Control System for Highly Variable Environments
    Richardson, G. A.
    JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME, 2009, 131 (06): : 1 - 3
  • [2] VARIABLE GAIN ADAPTIVE-CONTROL SYSTEM FOR TURNING
    MASORY, O
    KOREN, Y
    JOURNAL OF MANUFACTURING SYSTEMS, 1983, 2 (02) : 165 - 173
  • [3] The impact of different gain control methods on performance of CMOS variable-gain LNA
    Su, Hsiao Wei
    Wang, Zhi Hua
    2007 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, 2007, : 2208 - +
  • [4] Neural control system for a swarm of autonomous underwater vehicles
    Praczyk, Tomasz
    KNOWLEDGE-BASED SYSTEMS, 2023, 276
  • [5] Neural network control for tele-rehabilitation robot based on variable gain
    Guo Xiaobo
    Song Aiguo
    Zhai Yan
    BMEI 2008: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS, VOL 2, 2008, : 778 - +
  • [6] Neural network based autonomous control of a speech synthesis system
    Panagiotopoulos, Dimokritos
    Orovas, Christos
    Syndoukas, Dimitrios
    INTELLIGENT SYSTEMS WITH APPLICATIONS, 2022, 14
  • [7] Autonomous System Identification and Control Using Deep Neural Network
    Ghasemabadi, Amirhosein
    Mehmandar, Benyamin
    Kalhor, Ahmad
    2021 21ST INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2021), 2021, : 623 - 628
  • [8] Neural-fuzzy autonomous overland vehicle control system
    Ilyasov, BG
    Startsev, YV
    Golovatsky, KE
    Almukhametov, RR
    Belalov, BM
    8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING, 2001, : 947 - 950
  • [9] Variable template matching for autonomous mobile robot with hierarchical control system
    Abe, Y
    Shikano, M
    Fukuda, T
    Arai, F
    Tanaka, Y
    INTELLIGENT AUTONOMOUS SYSTEMS: IAS-5, 1998, : 368 - 375
  • [10] VARIABLE-GAIN FEEDBACK LINEARIZES CONTROL-SYSTEM RESPONSE
    DAY, KS
    ELECTRO-TECHNOLOGY, 1968, 81 (02): : 38 - &