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阻抗带:用于生理传感概念验证的人体耦合生物阻抗贴片

ImpediBands: Body Coupled Bio-Impedance Patches for Physiological Sensing Proof of Concept.

作者信息

Sel Kaan, Ibrahim Bassem, Jafari Roozbeh

出版信息

IEEE Trans Biomed Circuits Syst. 2020 Aug;14(4):757-774. doi: 10.1109/TBCAS.2020.2995810. Epub 2020 May 19.

Abstract

Continuous and robust monitoring of physiological signals is essential in improving the diagnosis and management of cardiovascular and respiratory diseases. The state-of-the-art systems for monitoring vital signs such as heart rate, heart rate variability, respiration rate, and other hemodynamic and respiratory parameters use often bulky and obtrusive systems or depend on wearables with limited sensing methods based on repetitive properties of the signals rather than the morphology. Moreover, multiple devices and modalities are typically needed for capturing various vital signs simultaneously. In this paper, we introduce ImpediBands: small-sized distributed smart bio-impedance (Bio-Z) patches, where the communication between the patches is established through the human body, eliminating the need for electrical wires that would create a common potential point between sensors. We use ImpediBands to collect instantaneous measurements from multiple locations over the chest at the same time. We propose a blind source separation (BSS) technique based on the second-order blind identification (SOBI) followed by signal reconstruction to extract heart and lung activities from the Bio-Z signals. Using the separated source signals, we demonstrate the performance of our system via providing strong confidence in the estimation of heart and respiration rates with low RMSE (HR = 0.579 beats per minute, RR = 0.285 breaths per minute), and high correlation coefficients (r = 0.948, r = 0.921).

摘要

持续且可靠地监测生理信号对于改善心血管和呼吸系统疾病的诊断与管理至关重要。用于监测诸如心率、心率变异性、呼吸频率以及其他血流动力学和呼吸参数等生命体征的现有系统,通常使用体积庞大且引人注目的设备,或者依赖于基于信号重复特性而非形态学的有限传感方法的可穿戴设备。此外,通常需要多个设备和模式才能同时捕捉各种生命体征。在本文中,我们介绍了ImpediBands:小型分布式智能生物阻抗(Bio-Z)贴片,贴片之间通过人体建立通信,无需电线,因为电线会在传感器之间形成公共电位点。我们使用ImpediBands同时从胸部的多个位置收集即时测量数据。我们提出一种基于二阶盲辨识(SOBI)的盲源分离(BSS)技术,随后进行信号重建,以从Bio-Z信号中提取心脏和肺部活动。利用分离出的源信号,我们通过对心率和呼吸率估计的低均方根误差(HR = 每分钟0.579次心跳,RR = 每分钟0.285次呼吸)和高相关系数(r = 0.948,r = 0.921)展示了我们系统的性能。

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