CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals

• URL: http://arxiv.org/abs/2502.17536v1
• Date: Mon, 24 Feb 2025 16:53:47 GMT
• Title: CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals
• Authors: Xiaoyan Li, Shixin Xu, Faisal Habib, Neda Aminnejad, Arvind Gupta, Huaxiong Huang,
• Abstract summary: The study addresses the challenge of reconstructing unseen ECG signals from PPG signals.<n>Data collection processes often introduce noise, complicating ECG reconstruction from PPG even with advanced machine learning models.<n>We develop a novel subject-independent PPG-to-ECG reconstruction model that integrates contrastive learning, adversarial learning, and attention gating.
• Score: 6.327810380749031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study addresses the challenge of reconstructing unseen ECG signals from PPG signals, a critical task for non-invasive cardiac monitoring. While numerous public ECG-PPG datasets are available, they lack the diversity seen in image datasets, and data collection processes often introduce noise, complicating ECG reconstruction from PPG even with advanced machine learning models. To tackle these challenges, we first introduce a novel synthetic ECG-PPG data generation technique using an ODE model to enhance training diversity. Next, we develop a novel subject-independent PPG-to-ECG reconstruction model that integrates contrastive learning, adversarial learning, and attention gating, achieving results comparable to or even surpassing existing approaches for unseen ECG reconstruction. Finally, we examine factors such as sex and age that impact reconstruction accuracy, emphasizing the importance of considering demographic diversity during model training and dataset augmentation.

Related papers

• Deep learning model for ECG reconstruction reveals the information content of ECG leads [0.0]
  This study introduces a deep learning model based on the U-net architecture to reconstruct missing leads in electrocardiograms (ECGs) Using publicly available datasets, the model was trained to regenerate 12-lead ECG data. The results highlight the ability of the model to quantify the information content of each ECG lead and their inter-lead correlations.
  arXiv  Detail & Related papers  (2025-02-01T21:06:07Z)
• Synthetic CT image generation from CBCT: A Systematic Review [44.01505745127782]
  Generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology.<n>A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT.
  arXiv  Detail & Related papers  (2025-01-22T13:54:07Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals. Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality. The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion [1.727597257312416]
  We focus on two main scenarios: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering entire leads from signal in another unique lead.<n>We propose ECGrecover, a neural network model trained on a novel composite objective function to address the reconstruction problem.
  arXiv  Detail & Related papers  (2024-05-31T15:17:12Z)
• Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction [42.95604565673447]
  This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models.<n>We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/ computed tomography (CT) datasets.
  arXiv  Detail & Related papers  (2024-04-12T18:21:08Z)
• 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)
• 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)
• Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection [0.0]
  We present an in-depth review of transformer architectures that are applied to ECG classification. These models capture complex temporal relationships in ECG signals that other models might overlook. This review serves as a valuable resource for researchers and practitioners and aims to shed light on this innovative application in ECG interpretation.
  arXiv  Detail & Related papers  (2023-06-02T03:23:16Z)
• 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)
• Performer: A Novel PPG to ECG Reconstruction Transformer For a Digital Biomarker of Cardiovascular Disease Detection [0.0]
  Cardiovascular diseases (CVDs) have become the top one cause of death; three-quarters of these deaths occur in lower-income communities. Electrocardiography (ECG) is infeasible for continuous cardiac monitoring due to its requirement for user participation. Photoplethysmography is easy to collect, but the limited accuracy constrains its clinical usage.
  arXiv  Detail & Related papers  (2022-04-25T17:10:13Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• 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)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

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.