Surface Impedance for Real-Time Cardiac Monitoring in Body Area Networks

Biomedical electronics are becoming increasingly important as a result of concerns with remote patient monitoring. It is becoming increasingly vital to follow cardiac patients outside of hospitals and in their daily lives. Therefore, wireless communication and home usage are suitable for medical devices. This study proposes an innovative technique for monitoring cardiac activity based on variation in surface impedance during cardiac muscle activity. A human thoracic model was rigorously created to justify our approach, confirming efficiency of the technique. This model has a three-layered structure, with each layer distinguished by its distinct surface impedances. As one moves from the deepest to the outermost layer, the impedance of each layer changes. Therefore, it is crucial to understand the physical properties of biological tissues to predict their activity accurately. This work introduces an implementation of a novel approach, centered on analyzing the surface impedance variation within a multilayer model. This variation is intricately linked to the thickness of the innermost layer within the ISM band across four distinct frequencies: 433 MHz, 915 MHz, 2.4 GHz, and 5.8 GHz. These frequencies provide a balance between range, reliability, and compatibility with existing wireless technologies, ensuring effective and robust communication for applications.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2024-12-23
DOI
https://doi.org/10.1109/access.2024.3521041
Primary Topic
Wireless Body Area Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Surface Impedance for Real-Time Cardiac Monitoring in Body Area Networks

Omar Cheikhrouhou, Hamadi Ghariani, Anis Koubâa, Imen Jarraya et al.
IEEE Access
Wireless Body Area Networks
article

Surface Impedance for Real-Time Cardiac Monitoring in Body Area Networks

Omar Cheikhrouhou, Hamadi Ghariani, Anis Koubâa, Imen Jarraya, Khaoula Tayari
article en

Abstract

Biomedical electronics are becoming increasingly important as a result of concerns with remote patient monitoring. It is becoming increasingly vital to follow cardiac patients outside of hospitals and in their daily lives. Therefore, wireless communication and home usage are suitable for medical devices. This study proposes an innovative technique for monitoring cardiac activity based on variation in surface impedance during cardiac muscle activity. A human thoracic model was rigorously created to justify our approach, confirming efficiency of the technique. This model has a three-layered structure, with each layer distinguished by its distinct surface impedances. As one moves from the deepest to the outermost layer, the impedance of each layer changes. Therefore, it is crucial to understand the physical properties of biological tissues to predict their activity accurately. This work introduces an implementation of a novel approach, centered on analyzing the surface impedance variation within a multilayer model. This variation is intricately linked to the thickness of the innermost layer within the ISM band across four distinct frequencies: 433 MHz, 915 MHz, 2.4 GHz, and 5.8 GHz. These frequencies provide a balance between range, reliability, and compatibility with existing wireless technologies, ensuring effective and robust communication for applications.

IEEE AccessVol. 13
Prince Sultan University (SA), University of Sfax (TN)
Affordable and clean energy
Openalex Percentile: Top 78%
Wireless Body Area Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.