Compression of ECG (electrocardiogram) as a signal with finite rate of innovation (FRI) is proposed in this paper. By modelling the ECG signal as the sum of bandlimited and nonuniform linear spline which contains finite rate of innovation (FRI), sampling theory is applied to achieve effective compression and reconstruction of ECG signal. The simulation results show that the performance of the algorithm is quite satisfactory in preserving the diagnostic information as compared to the classical sampling scheme which uses the sinc interpolation.
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http://dx.doi.org/10.1109/IEMBS.2005.1616262 | DOI Listing |
Physiol Meas
January 2025
Harbin Institute of Technology, Harbin Institute of Technology, Harbin, 150001, CHINA.
Objective: The demand for ECG datasets, particularly those containing rare classes, poses a significant challenge as deep learning becomes increasingly prevalent in ECG signal research. While Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are widely adopted, they encounter difficulties in effectively generating samples for classes with limited instances.
Approach: To address this issue, we propose a novel Feature Disentanglement Auto-Encoder (FDAE) designed to dissect various generative factors under a contrastive learning framework within ECG data to facilitate the generation of new ECG samples.
Biomed Phys Eng Express
January 2025
Electronics and Communication Engineering, Rajiv Gandhi University, Rono Hills, Doimukh, ITANAGAR, Itanagar, Arunachal Pradesh, 791112, INDIA.
Accurate detection of cardiac arrhythmias is crucial for preventing premature deaths. The current study employs a dual-stage Discrete Wavelet Transform (DWT) and a median filter to eliminate noise from ECG signals. Subsequently, ECG signals are segmented, and QRS regions are extracted for further preprocessing.
View Article and Find Full Text PDFBMC Cardiovasc Disord
January 2025
ITACA Institute, Universitat Politècnica de València, València, Spain.
Background: Complexity and signal recurrence metrics obtained from body surface potential mapping (BSPM) allow quantifying atrial fibrillation (AF) substrate complexity. This study aims to correlate electrocardiographic imaging (ECGI) detected reentrant patterns with BSPM-calculated signal complexity and recurrence metrics.
Methods: BSPM signals were recorded from 28 AF patients (17 male, 11 women, 62.
Microsyst Nanoeng
January 2025
Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education, North University of China, 030051, Taiyuan, China.
The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection solutions. Traditional methods, often costly, bulky, and prone to subjectivity, fall short of meeting the need for daily monitoring. Digital and portable wearable monitoring devices have emerged as a promising research frontier.
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