One of the best ways to obtain health information is from an electrocardiogram (ECG). Through an ECG, characteristics such as patients' heartbeats, heart conditions, and heart disease can be analyzed. Unfortunately, most available healthcare devices do not provide clinical data such as information regarding patients' heart activities. Many researchers have tried to solve this problem by inventing wearable heart monitoring systems with a chest strap or wristband, but their performances were not feasible for practical applications. Thus, the aim of this study is to build a new system to monitor heart activity through ECG signals. The proposed system consists of capacitive-coupled electrodes embedded in an armband. It is considered to be a reliable, robust, and low-power-transmission ECG monitoring system. The reliability of this system was achieved by the careful placement of sensors in the armband. Bluetooth low energy (BLE) was used as the protocol for data transmission; this protocol was proposed to develop the low-power-transmission system. For robustness, the proposed system is equipped with analysis capabilities-e.g., real-time heartbeat detection and a filter algorithm to ignore distractions from body movements or noise from the environment.
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http://dx.doi.org/10.1109/TBCAS.2016.2519523 | DOI Listing |
PLoS One
January 2025
Tactical Research Unit, Bond University, Robina, QLD, Australia.
Police tactical group (PTG) officers respond to the most demanding and high-risk police situations. As such, PTG personnel require exceptional physical fitness, and selection for employment often evaluates fitness both directly and indirectly. While heart rate (HR) is often used to measure physical effort, heart rate variability (HRV) may be a valuable tool for measuring stress holistically.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy.
Diabetes is a chronic condition, and traditional monitoring methods are invasive, significantly reducing the quality of life of the patients. This study proposes the design of an innovative system based on a microcontroller that performs real-time ECG acquisition and evaluates the presence of diabetes using an Edge-AI solution. A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFSci Rep
January 2025
National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, common in dialysis patients, can lead to life-threatening arrhythmias or sudden death if untreated. Therefore, real-time monitoring of hyperkalemia in this population is crucial.
View Article and Find Full Text PDFTalanta
January 2025
Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, 300072, China; School of Exercise and Health, Tianjin University of Sport, Tianjin, 300211, China. Electronic address:
Developing a wearable device that can continuously and reliably detect and evaluate heart rate variability (HRV) parameters is critical for the diabetic population with cardiac autonomic neuropathy (CAN). In this work, we proposed a zwitterionic conducting hydrogel that enabled a reliable and comfortable wearable device for the evaluation and detection of the autonomic nervous system (ANS). The hydrogel can achieve a strain of 2003 %, an electrical conductivity of 190 mS/m, and is capable of adhering to a variety of materials, including wood, plastic, and glass.
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