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Wearable and Noninvasive Device for Integral Congestive Heart Failure Management in the IoMT Paradigm

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2023 Aug 26
PMID 37631594
Authors
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Abstract

Noninvasive remote monitoring of hemodynamic variables is essential in optimizing treatment opportunities and predicting rehospitalization in patients with congestive heart failure. The objective of this study is to develop a wearable bioimpedance-based device, which can provide continuous measurement of cardiac output and stroke volume, as well as other physiological parameters for a greater prognosis and prevention of congestive heart failure. The bioimpedance system, which is based on a robust and cost-effective measuring principle, was implemented in a CMOS application specific integrated circuit, and operates as the analog front-end of the device, which has been provided with a radio-frequency section for wireless communication. The operating parameters of the proposed wearable device are remotely configured through a graphical user interface to measure the magnitude and the phase of complex impedances over a bandwidth of 1 kHz to 1 MHz. As a result of this study, a cardiac activity monitor was implemented, and its accuracy was evaluated in 33 patients with different heart diseases, ages, and genders. The proposed device was compared with a well-established technique such as Doppler echocardiography, and the results showed that the two instruments are clinically equivalent.

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References
1.
Ben Atitallah B, Kallel A, Bouchaala D, Derbel N, Kanoun O . Comparative Study of Measurement Methods for Embedded Bioimpedance Spectroscopy Systems. Sensors (Basel). 2022; 22(15). PMC: 9371087. DOI: 10.3390/s22155801. View

2.
Passing H, Bablok W . Comparison of several regression procedures for method comparison studies and determination of sample sizes. Application of linear regression procedures for method comparison studies in Clinical Chemistry, Part II. J Clin Chem Clin Biochem. 1984; 22(6):431-45. DOI: 10.1515/cclm.1984.22.6.431. View

3.
Urban M, Klum M, Pielmus A, Liebrenz F, Mann S, Tigges T . GRU Neural Network Improved Bioimpedance Based Stroke Volume Estimation during Ergometry Stress Test. Sensors (Basel). 2022; 22(20). PMC: 9612153. DOI: 10.3390/s22207883. View

4.
Tronstad C, Hogetveit J, Elvebakk O, Kalvoy H . Age-related Differences in the Morphology of the Impedance Cardiography Signal. J Electr Bioimpedance. 2021; 10(1):139-145. PMC: 7851975. DOI: 10.2478/joeb-2019-0020. View

5.
Cybulski G, Strasz A, Niewiadomski W, Gasiorowska A . Impedance cardiography: recent advancements. Cardiol J. 2012; 19(5):550-6. DOI: 10.5603/cj.2012.0104. View