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.

Download full-text PDF

Source
http://dx.doi.org/10.1109/IEMBS.2005.1616262DOI Listing

Publication Analysis

Top Keywords

ecg signal
12
finite rate
12
rate innovation
12
compression ecg
8
signal finite
8
innovation fri
8
signal
4
innovation compression
4
ecg electrocardiogram
4
electrocardiogram signal
4

Similar Publications

ECG signal generation using feature disentanglement auto-encoder.

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.

View Article and Find Full Text PDF

Automated Classification of Cardiac Arrhythmia using Short-Duration ECG Signals and Machine Learning.

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 PDF

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.

View Article and Find Full Text PDF

A novel wearable device integrating ECG and PCG for cardiac health monitoring.

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!