Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

• URL: http://arxiv.org/abs/2308.13568v2
• Date: Thu, 28 Dec 2023 04:37:54 GMT
• Title: Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation
• Authors: Debaditya Shome, Pritam Sarkar, Ali Etemad
• Abstract summary: Region-Disentangled Diffusion Model captures complex temporal dynamics of ECG signals. New model can generate high-fidelity ECG from PPG in as few as 10 diffusion steps. New model is first diffusion model for cross-modal signal-to-signal in bio-signal domain.
• Score: 29.706347050700867
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The high prevalence of cardiovascular diseases (CVDs) calls for accessible and cost-effective continuous cardiac monitoring tools. Despite Electrocardiography (ECG) being the gold standard, continuous monitoring remains a challenge, leading to the exploration of Photoplethysmography (PPG), a promising but more basic alternative available in consumer wearables. This notion has recently spurred interest in translating PPG to ECG signals. In this work, we introduce Region-Disentangled Diffusion Model (RDDM), a novel diffusion model designed to capture the complex temporal dynamics of ECG. Traditional Diffusion models like Denoising Diffusion Probabilistic Models (DDPM) face challenges in capturing such nuances due to the indiscriminate noise addition process across the entire signal. Our proposed RDDM overcomes such limitations by incorporating a novel forward process that selectively adds noise to specific regions of interest (ROI) such as QRS complex in ECG signals, and a reverse process that disentangles the denoising of ROI and non-ROI regions. Quantitative experiments demonstrate that RDDM can generate high-fidelity ECG from PPG in as few as 10 diffusion steps, making it highly effective and computationally efficient. Additionally, to rigorously validate the usefulness of the generated ECG signals, we introduce CardioBench, a comprehensive evaluation benchmark for a variety of cardiac-related tasks including heart rate and blood pressure estimation, stress classification, and the detection of atrial fibrillation and diabetes. Our thorough experiments show that RDDM achieves state-of-the-art performance on CardioBench. To the best of our knowledge, RDDM is the first diffusion model for cross-modal signal-to-signal translation in the bio-signal domain.

Related papers

• From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data [11.914561937089214]
  This work will focus on radar-based ECG recovery in new scenarios with limited data.<n>A transfer learning model (RFcardi) is proposed to extract cardio-related information from the radar signal without ECG ground truth.<n>RFcardi model can effectively generate faithful ECG recoveries after using a small number of radar-ECG pairs for training.
  arXiv  Detail & Related papers  (2025-06-24T06:38:34Z)
• A Complex UNet Approach for Non-Invasive Fetal ECG Extraction Using Single-Channel Dry Textile Electrodes [40.11095094521714]
  The fetal electrocardiogram (fECG) represents a promising tool for assessing fetal health beyond clinical environments.<n>This setup presents many challenges, including increased noise and motion artefacts, which complicate the accurate extraction of fECG signals.<n>We introduce a pioneering method for extracting fECG from single-channel recordings obtained using dry textile electrodes using AI techniques.
  arXiv  Detail & Related papers  (2025-06-16T20:35:52Z)
• Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer [41.095491708125515]
  We propose UniCardio, a multi-modal diffusion transformer that reconstructs low-quality signals and synthesizes unrecorded signals.<n>By exploiting the complementary nature of cardiovascular signals, UniCardio clearly outperforms recent task-specific baselines in signal denoising, imputation, and translation.<n>These advantages position UniCardio as a promising avenue for advancing AI-assisted healthcare.
  arXiv  Detail & Related papers  (2025-05-28T12:45:39Z)
• Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases [1.732458484303615]
  Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. We present an avant-garde methodology that synergistically integrates ECG readings and fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Preliminary tests yielded a commendable accuracy of 84 percent, underscoring the potential of this combined diagnostic strategy.
  arXiv  Detail & Related papers  (2025-03-31T07:53:36Z)
• Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models [45.94962431110573]
  Camera-based monitoring of vital signs, also known as imaging photoplethysmography (i), has seen applications in driver-monitoring, affective computing, and more. We introduce methods that combine signal processing and deep learning methods in an inverse problem.
  arXiv  Detail & Related papers  (2025-03-21T16:11:21Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration [50.602074919305636]
  This paper introduces a lightweight end-to-end Cardiac Ultrasound frame-to-volume Registration network, termed CU-Reg. We use epicardium prompt-guided anatomical clues to reinforce the interaction of 2D sparse and 3D dense features, followed by a voxel-wise local-global aggregation of enhanced features.
  arXiv  Detail & Related papers  (2024-06-20T17:47:30Z)
• Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals [4.056982620027252]
  We develop a vision transformer approach to identify atrial fibrillation based on single-lead ECG data. A residual network (ResNet) approach is also developed for comparison with the vision transformer approach.
  arXiv  Detail & Related papers  (2024-02-12T11:04:08Z)
• 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)
• 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)
• PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling [11.617950008187366]
  Photoplethysmography ( PPG) is a cost-effective and non-invasive technique that utilizes optical methods to measure cardiac physiology. Here, we propose a subject-independent attention-based deep state-space model (ADSSM) to translate PPG signals to corresponding ECG waveforms.
  arXiv  Detail & Related papers  (2023-09-27T03:07:46Z)
• 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)
• Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation [44.868281669589194]
  In order to improve the artery segmentation accuracy and stability during scans, this work presents a novel pulsation-assisted segmentation neural network (PAS-NN) Motion magnification techniques are employed to amplify the subtle motion within the frequency band of interest to extract the pulsation signals from sequential US images. The extracted real-time pulsation information can help to locate the arteries on cross-section US images.
  arXiv  Detail & Related papers  (2023-07-07T16:14:17Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal [4.998493052085877]
  Electrocardiogram (ECG) signals commonly suffer noise interference, such as baseline wander. This paper proposes a novel ECG baseline wander and noise removal technology.
  arXiv  Detail & Related papers  (2022-07-31T23:39:33Z)
• A Novel Transfer Learning-Based Approach for Screening Pre-existing Heart Diseases Using Synchronized ECG Signals and Heart Sounds [0.5621251909851629]
  Diagnosing pre-existing heart diseases early in life is important to prevent complications such as pulmonary hypertension, heart rhythm problems, blood clots, heart failure and sudden cardiac arrest. To identify such diseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey important information. Here, we evaluate this hypothesis on a subset of the PhysioNet Challenge 2016 dataset which contains simultaneously acquired PCG and ECG recordings. Our novel Dual-Convolutional Neural Network based approach uses transfer learning to tackle the problem of having limited amounts of simultaneous PCG and ECG data that is publicly available.
  arXiv  Detail & Related papers  (2021-02-02T19:51:12Z)

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.