Measurement of Fractional Order Model Parameters of Respiratory Mechanical Impedance in Total Liquid Ventilation

被引:21
|
作者
Beaulieu, Alexandre [1 ]
Bosse, Dominick [2 ]
Micheau, Philippe [1 ]
Avoine, Olivier [2 ]
Praud, Jean-Paul [2 ]
Walti, Herve [2 ]
机构
[1] Univ Sherbrooke, Dept Mech Engn, Sherbrooke, PQ J1K 2R1, Canada
[2] Univ Sherbrooke, Dept Pediat, Sherbrooke, PQ J1K 2R1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Biomedical systems; forced oscillation technique; frequency-domain system identification; lung mechanics; signal processing; FORCED OSCILLATION TECHNIQUE; ASSISTED VENTILATION; INPUT IMPEDANCE; LUNG-TISSUE; SYSTEM; PERFLUOROCARBON; DESIGN; GAS;
D O I
10.1109/TBME.2011.2169257
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This study presents a methodology for applying the forced-oscillation technique in total liquid ventilation. It mainly consists of applying sinusoidal volumetric excitation to the respiratory system, and determining the transfer function between the delivered flow rate and resulting airway pressure. The investigated frequency range was f is an element of [0.05, 4] Hz at a constant flow amplitude of 7.5 mL/s. The five parameters of a fractional order lung model, the existing "5-parameter constant-phase model," were identified based on measured impedance spectra. The identification method was validated in silico on computer-generated datasets and the overall process was validated in vitro on a simplified single-compartment mechanical lung model. In vivo data on ten newborn lambs suggested the appropriateness of a fractional-order compliance term to the mechanical impedance to describe the low-frequency behavior of the lung, but did not demonstrate the relevance of a fractional-order inertance term. Typical respiratory system frequency response is presented together with statistical data of the measured in vivo impedance model parameters. This information will be useful for both the design of a robust pressure controller for total liquid ventilators and the monitoring of the patient's respiratory parameters during total liquid ventilation treatment.
引用
收藏
页码:323 / 331
页数:9
相关论文
共 50 条
  • [21] LUNG AND RESPIRATORY IMPEDANCE AT LOW-FREQUENCY DURING MECHANICAL VENTILATION IN RABBITS
    ROTGER, M
    PESLIN, R
    NAVAJAS, D
    FARRE, R
    JOURNAL OF APPLIED PHYSIOLOGY, 1995, 78 (06) : 2153 - 2160
  • [22] ADAPTATION OF THE FORCED OSCILLATION TECHNIQUE FOR MEASUREMENT OF TOTAL RESPIRATORY IMPEDANCE IN CALVES
    GUSTIN, P
    LANDSER, FJ
    LOMBA, F
    BULLETIN EUROPEEN DE PHYSIOPATHOLOGIE RESPIRATOIRE-CLINICAL RESPIRATORY PHYSIOLOGY, 1987, 23 : S323 - S323
  • [23] Electroviscoelasticity of liquid/liquid interfaces: fractional-order model
    Spasic, AM
    Lazarevic, MP
    JOURNAL OF COLLOID AND INTERFACE SCIENCE, 2005, 282 (01) : 223 - 230
  • [24] Measurement estimation of fractional-order model parameters for a new class of symmetrical polymer supercapacitors
    Gocki, Michal
    Jakubowska-Ciszek, Agnieszka
    Pruski, Piotr
    PRZEGLAD ELEKTROTECHNICZNY, 2022, 98 (11): : 224 - 228
  • [25] Physics-based fractional-order model and parameters identification of liquid metal battery
    Shi, Qionglin
    Guo, Zhenlin
    Wang, Sheng
    Yan, Shuai
    Zhou, Xianbo
    Li, Haomiao
    Wang, Kangli
    Jiang, Kai
    ELECTROCHIMICA ACTA, 2022, 428
  • [26] Research on the Identification Method of Respiratory Characteristic Parameters during Mechanical Ventilation
    Zhang, Yuxin
    Bai, Jing
    Ma, Xingyi
    Xu, Yu
    PROCESSES, 2024, 12 (08)
  • [27] DETECTION OF RESPIRATORY MECHANICAL DYSFUNCTION BY FORCED RANDOM NOISE IMPEDANCE PARAMETERS
    HAYES, DA
    PIMMEL, RL
    FULLTON, JM
    BROMBERG, PA
    AMERICAN REVIEW OF RESPIRATORY DISEASE, 1979, 120 (05): : 1095 - 1100
  • [29] Online simultaneous identification of parameters and order of a fractional order battery model
    Tian, Jinpeng
    Xiong, Rui
    Shen, Weixiang
    Wang, Ju
    Yang, Ruixin
    JOURNAL OF CLEANER PRODUCTION, 2020, 247
  • [30] Mechanical ventilation guided by electrical impedance tomography in pediatric acute respiratory distress syndrome
    Jeffrey Dmytrowich
    Tanya Holt
    Karen Schmid
    Gregory Hansen
    Journal of Clinical Monitoring and Computing, 2018, 32 : 503 - 507