Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11428
Title: Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure An Overview
Authors: Salman, Hüseyin Enes
Al-Ruweidi, Mahmoud Khatib A.A.
Ouakad, Hassen M.
Yalçın, Hüseyin Çağatay
Publisher: John Wiley & Sons Ltd.
Source: Salman, H. E., Al‐Ruweidi, M. K. A., Ouakad, H. M., & Yalcin, H. C. (2022). Minimally Invasive and Non‐Invasive Sensor Technologies for Predicting Heart Failure: An Overview. Predicting Heart Failure: Invasive, Non‐Invasive, Machine Learning and Artificial Intelligence Based Methods, 109-138.
Abstract: This chapter explains the non-invasive and minimally invasive sensor technologies and techniques employed for heart failure (HF) diagnosis. It summarizes landmark studies and clinical trials which prove the potential of non-invasive monitoring of HF patients. The methods for identifying worsening HF can be listed as body weight measurements, electrocardiography (ECG), bioimpedance monitoring, activity tracking, implanted pressure sensors, lung ultrasound monitoring, measurements with sound and Doppler sensors, seismocardiography, ballistocardiography, photoplethysmography, and measurement of natriuretic peptides levels in circulating blood. It is necessary to elucidate the effect of remote monitoring modalities for HF prediction on large-scale randomized control trials. A relative increase in thoracic bioimpedance provides better prediction of HF-related congestion. ECG is among the under-investigated techniques for HF remote monitoring. The positive results of clinical trials with large numbers of patients show the high potential of non-invasive sensors for diagnosing HF.
URI: https://doi.org/10.1002/9781119813040.ch5
https://hdl.handle.net/20.500.11851/11428
ISBN: 9781119813040
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering

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