Review of noise removal techniques in ECG signals
An electrocardiogram (ECG) records the electrical signal from the heart to check for different
heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre …
heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre …
A survey on ECG analysis
The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
[HTML][HTML] Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network
F Zhu, F Ye, Y Fu, Q Liu, B Shen - Scientific reports, 2019 - nature.com
Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are
used to help diagnose heart disease by recording the heart's activity. However, automated …
used to help diagnose heart disease by recording the heart's activity. However, automated …
An open source benchmarked toolbox for cardiovascular waveform and interval analysis
Objective: This work aims to validate a set of data processing methods for variability metrics,
which hold promise as potential indicators for autonomic function, prediction of adverse …
which hold promise as potential indicators for autonomic function, prediction of adverse …
ECG pattern analysis for emotion detection
F Agrafioti, D Hatzinakos… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
Emotion modeling and recognition has drawn extensive attention from disciplines such as
psychology, cognitive science, and, lately, engineering. Although a significant amount of …
psychology, cognitive science, and, lately, engineering. Although a significant amount of …
Deep learning models for electrocardiograms are susceptible to adversarial attack
Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial
devices, necessitating the development of automated interpretation strategies. Recently …
devices, necessitating the development of automated interpretation strategies. Recently …
Multiscale energy and eigenspace approach to detection and localization of myocardial infarction
LN Sharma, RK Tripathy… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is
proposed for the detection and localization of myocardial infarction (MI) from multilead …
proposed for the detection and localization of myocardial infarction (MI) from multilead …
SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead …
Electrocardiography (ECG) is a sophisticated tool for the diagnosis of myocardial infarction
(MI). Deep learning approaches can support MI diagnosis based on ECG data. However …
(MI). Deep learning approaches can support MI diagnosis based on ECG data. However …
[HTML][HTML] DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
Recent global developments underscore the prominent role big data have in modern
medical science. But privacy issues constitute a prevalent problem for collecting and sharing …
medical science. But privacy issues constitute a prevalent problem for collecting and sharing …
[HTML][HTML] A review of fetal ECG signal processing; issues and promising directions
R Sameni, GD Clifford - The open pacing, electrophysiology & …, 2010 - ncbi.nlm.nih.gov
The field of electrocardiography has been in existence for over a century, yet despite
significant advances in adult clinical electrocardiography, signal processing techniques and …
significant advances in adult clinical electrocardiography, signal processing techniques and …