Ambulatory electrocardiogram (ECG) recorders are increasingly in use by people suffering from cardiac abnormalities. However, the ECG signal acquired by the ambulatory recorder is influenced by motion artifacts induced by any body movement activity (BMA). The goal of the paper is to demonstrate that it is possible to determine the BMA from the motion artifacts in the ECG signal itself. The ECG signal during a specific BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts induced due to the BMA and sensor noise. We propose to characterize and determine the BMA from the corresponding motion artifact data in the ECG signal itself. The proposed technique is useful for removal of motion artifacts from the ECG signals for ambulatory cardiac monitoring.
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http://dx.doi.org/10.1109/IEMBS.2006.259315 | DOI Listing |
Sensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
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January 2025
CAS Center for Excellence in Superconducting Electronics (CENSE), Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, Shanghai 200050, China.
Generally, the electrocardiography (ECG) system plays an important role in preventing and diagnosing heart diseases. To further improve the amenity and convenience of using an ECG system, we built a customized capacitive electrocardiography (cECG) system with one wet electrode, sixteen non-contact electrodes, two ADS1299 chips, and one STM32F303-based microcontroller unit (MCU). This new cECG system could acquire, save, and display the ECG data in real time.
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January 2025
School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130102, People's Republic of China.
Atrial fibrillation (AF) is a common arrhythmia disease with a higher incidence rate. The diagnosis of AF is time-consuming. Although many ECG classification models have been proposed to assist in AF detection, they are prone to misclassifying indistinguishable noise signals, and the context information of long-term signals is also ignored, which impacts the performance of AF detection.
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).
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