We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




Non-Invasive RF Technology Enables Long-Term Cardiac Monitoring

By HospiMedica International staff writers
Posted on 31 Dec 2024
Print article
Image: Application scenarios of the monitoring system (Photo courtesy of USTC)
Image: Application scenarios of the monitoring system (Photo courtesy of USTC)

Cardiovascular diseases (CVDs) are the leading global cause of death, responsible for approximately 17.9 million deaths each year. The growing aging population has further contributed to the rising prevalence and mortality rates associated with CVDs. Studies show that early detection and timely intervention can significantly reduce the occurrence of cardiovascular illnesses. However, current detection methods such as electrocardiograms (ECG) and Holter monitoring are accurate but have limitations. The electrodes used in ECG and Holter devices can cause discomfort, making them impractical for long-term use. While wearable devices offer greater convenience, they tend to be less precise and more susceptible to environmental interference. Now, researchers have made a significant advancement in cardiovascular monitoring with the development of a non-invasive radio frequency (RF) based system that can accurately measure heart rate variability (HRV) over extended periods.

The RF-HRV system, developed by a team at the University of Science and Technology of China (USTC, Anhui, China), addresses interference caused by respiratory motion in far-field conditions by analyzing RF signals. The system utilizes a signal selection algorithm that identifies signals containing rich heartbeat information from multiple reflected sources. It then applies the variational mode decomposition (VMD) algorithm to extract high-frequency components, enabling the system to accurately capture clear heartbeat patterns. By combining adjacent heartbeat harmonics, the system generates specific patterns corresponding to the heart rate, which are then used to calculate HRV.

In addition, the researchers tested the system in a large-scale outpatient setting involving 6,222 participants, as well as in a long-term daily scenario involving continuous multi-night sleep monitoring. The study, published in Nature Communications, demonstrated that in the outpatient setting, the system's median real-time inter-beat interval (RT-IBI) error was 26.1 milliseconds, and in the daily monitoring scenario, the error was 34.1 milliseconds—both showing significant improvements over existing systems that focus only on signals from the heart rate frequency band. The system also performs well in automatically detecting heartbeat abnormalities and offers performance comparable to clinical-grade 12-lead ECG systems.

This study's innovation lies in its departure from traditional signal processing methods. By utilizing high-frequency ranges, beyond 10th-order heartbeat harmonics, the system effectively isolates heartbeat signals and overcomes the challenges posed by respiratory motion interference. This breakthrough establishes a strong foundation for the use of millimeter-wave radar in cardiac monitoring, enabling long-term, non-invasive monitoring without the need for electrodes or frequent adjustments. This advancement promises to provide a more comfortable, practical solution for cardiovascular care.

Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
New
Disaster Preparedness Bed
Disaster Preparedness Bed
New
Mobile Cordless Cast Saw
CleanCast CSB-100

Print article

Channels

Surgical Techniques

view channel
Image: The groundbreaking tool can transform outcomes for stroke intervention (Photo courtesy of Shutterstock)

Groundbreaking Tool Accurately Predicts Stroke Outcome for Better Carotid Surgery Decisions

Stroke continues to be a significant global health concern, ranking as the second leading cause of mortality worldwide. Timely intervention is crucial for stroke patients, with carotid endarterectomy (CEA)... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.