Representing and Denoising Wearable ECG Recordings
- URL: http://arxiv.org/abs/2012.00110v1
- Date: Mon, 30 Nov 2020 21:33:11 GMT
- Title: Representing and Denoising Wearable ECG Recordings
- Authors: Jeffrey Chan, Andrew C. Miller, Emily B. Fox
- Abstract summary: We develop a statistical model to simulate a structured noise process in ECGs derived from a wearable sensor.
We design a beat-to-beat representation that is conducive for analyzing variation, and devise a factor analysis-based method to denoise the ECG.
- Score: 12.378631176671773
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modern wearable devices are embedded with a range of noninvasive biomarker
sensors that hold promise for improving detection and treatment of disease. One
such sensor is the single-lead electrocardiogram (ECG) which measures
electrical signals in the heart. The benefits of the sheer volume of ECG
measurements with rich longitudinal structure made possible by wearables come
at the price of potentially noisier measurements compared to clinical ECGs,
e.g., due to movement. In this work, we develop a statistical model to simulate
a structured noise process in ECGs derived from a wearable sensor, design a
beat-to-beat representation that is conducive for analyzing variation, and
devise a factor analysis-based method to denoise the ECG. We study synthetic
data generated using a realistic ECG simulator and a structured noise model. At
varying levels of signal-to-noise, we quantitatively measure an upper bound on
performance and compare estimates from linear and non-linear models. Finally,
we apply our method to a set of ECGs collected by wearables in a mobile health
study.
Related papers
- MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution [27.433941157026737]
We propose MSECG, a compact neural network model designed for ECG SR.
MSECG combines the strength of the recurrent Mamba model with convolutional layers to capture both local and global dependencies in ECG waveforms.
Experimental results show that MSECG outperforms two contemporary ECG SR models under both clean and noisy conditions.
arXiv Detail & Related papers (2024-12-06T08:53:31Z) - MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal [23.040957989796155]
We propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E)
MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions.
It requires less inference time than state-of-the-art diffusion-based ECG denoisers.
arXiv Detail & Related papers (2024-09-27T15:22:44Z) - In-ear ECG Signal Enhancement with Denoising Convolutional Autoencoders [11.901601030527862]
In-ear ECG recordings often suffer from significant noise due to their small amplitude and the presence of other physiological signals.
This study develops a denoising convolutional autoencoder to enhance ECG information from in-ear recordings, producing cleaner ECG outputs.
arXiv Detail & Related papers (2024-08-27T16:50:57Z) - MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [41.324530807795256]
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions.
Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation.
We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
arXiv Detail & Related papers (2024-03-07T23:20:56Z) - Bayesian ECG reconstruction using denoising diffusion generative models [11.603515105957461]
We propose a denoising diffusion generative model (DDGM) trained with healthy electrocardiogram (ECG) data.
Our results show that this innovative generative model can successfully generate realistic ECG signals.
arXiv Detail & Related papers (2023-12-18T15:56:21Z) - Improving Diffusion Models for ECG Imputation with an Augmented Template
Prior [43.6099225257178]
noisy and poor-quality recordings are a major issue for signals collected using mobile health systems.
Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models.
We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
arXiv Detail & Related papers (2023-10-24T11:34:15Z) - EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient
Arrhythmia Classification [79.7946379395238]
We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification.
We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
arXiv Detail & Related papers (2023-10-24T02:37:49Z) - DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial
Attention Detection [49.196182908826565]
Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment.
Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images.
This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
arXiv Detail & Related papers (2023-09-07T13:43:46Z) - Digital twinning of cardiac electrophysiology models from the surface
ECG: a geodesic backpropagation approach [39.36827689390718]
We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem.
We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case.
Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
arXiv Detail & Related papers (2023-08-16T14:57:12Z) - Leveraging Statistical Shape Priors in GAN-based ECG Synthesis [3.3482093430607267]
We propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling.
Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals.
Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines.
arXiv Detail & Related papers (2022-10-22T18:06:11Z) - ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed
Quality Labeling Using Neural Networks [69.25956542388653]
Deep learning (DL) algorithms are gaining weight in academic and industrial settings.
We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework.
The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
arXiv Detail & Related papers (2020-05-11T16:29:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.