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Blind Identification of Two-Channel IIR Systems With Application to Central Cardiovascular Monitoring

机译:两通道IIR系统的盲识别及其在中央心血管监测中的应用

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This paper presents a new approach to blind identification of a class of two-channel infinite impulse response (IIR) systems with applicability to clinical cardiovascular monitoring. Specifically, this paper deals with a class of two-channel IIR systems describing wave propagation dynamics. For this class of systems, this paper first derives a blind identifiability condition and develops a blind identification algorithm, which is able to determine both the numerator and denominator polynomials of the channel dynamics uniquely. This paper also develops a new input signal deconvolution algorithm that can reconstruct the input signal from the identified two-channel dynamics and the associated two-channel measurements. These methods are applied to identify the pressure wave propagation dynamics in the cardiovascular system and reconstruct the aortic blood pressure and flow signals from blood pressure measurements taken at two distinct extremity locations. Persistent excitation, model identifiability, and asymptotic variance are analyzed to quantify the method's validity, accuracy, and reliability without employing direct measurement of the aortic blood pressure and flow signals. The experimental results based on 83 data segments obtained from a swine subject illustrate how the cardiovascular dynamics can be identified accurately and reliably, and the aortic blood pressure and flow signals can be stably reconstructed from two distinct peripheral blood pressure signals under diverse physiologic conditions.
机译:本文提出了一种新的方法来盲目识别一类两通道无限冲激响应(IIR)系统,适用于临床心血管监测。具体而言,本文涉及描述波传播动力学的一类两通道IIR系统。对于此类系统,本文首先推导了盲可识别条件,并开发了一种盲识别算法,该算法能够唯一确定信道动力学的分子和分母多项式。本文还开发了一种新的输入信号去卷积算法,该算法可以从已识别的两通道动力学特性和相关的两通道测量结果重建输入信号。这些方法适用于识别压力波在心血管系统中的传播动力学,并根据在两个不同肢体位置进行的血压测量重建主动脉血压和流量信号。在不直接测量主动脉血压和流量信号的情况下,分析了持久性激发,模型可识别性和渐近方差以量化该方法的有效性,准确性和可靠性。基于从猪受试者获得的83个数据段的实验结果说明了如何准确,可靠地识别心血管动力学,并且可以在不同的生理条件下从两个不同的外周血压信号稳定地重建主动脉血压和流量信号。

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    Author(s):Jin-Oh HahnDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139Andrew T. ReisnerDepartment of Emergency Medicine, Massachusetts General Hospital, Boston, MA 02114H. Harry AsadaDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139;

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