Improving Diffusion Models for ECG Imputation with an Augmented Template Prior

• URL: http://arxiv.org/abs/2310.15742v2
• Date: Tue, 14 Nov 2023 12:02:43 GMT
• Title: Improving Diffusion Models for ECG Imputation with an Augmented Template Prior
• Authors: Alexander Jenkins, Zehua Chen, Fu Siong Ng, Danilo Mandic
• Abstract summary: 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.
• Score: 43.6099225257178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pulsative signals such as the electrocardiogram (ECG) are extensively collected as part of routine clinical care. However, noisy and poor-quality recordings are a major issue for signals collected using mobile health systems, decreasing the signal quality, leading to missing values, and affecting automated downstream tasks. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. Nevertheless, in comparison with the deterministic models, their performance is still limited, as the variations across subjects and heart-beat relationships are not explicitly considered in the training objective. In this work, to improve the imputation and forecasting accuracy for ECG with probabilistic 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. Specifically, 1) we first extract a subject-level pulsative template from the observed values to use as an informative prior of the missing values, which personalises the prior; 2) we then add beat-level stochastic shift terms to augment the prior, which considers variations in the position and amplitude of the prior at each beat; 3) we finally design a confidence score to consider the health condition of the subject, which ensures our prior is provided safely. Experiments with the PTBXL dataset reveal that PulseDiff improves the performance of two strong DDPM baseline models, CSDI and SSSD$^{S4}$, verifying that our method guides the generation of DDPMs while managing the uncertainty. When combined with SSSD$^{S4}$, PulseDiff outperforms the leading deterministic model for short-interval missing data and is comparable for long-interval data loss.

Related papers

• Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices [0.0]
  Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability. A 5-minute prediction window was chosen for timely intervention, with minute-levels standardizing the data. This study highlights ML's potential to improve triage and reduce alarm fatigue.
  arXiv  Detail & Related papers  (2024-10-30T23:24:28Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals [37.788535094404644]
  Atrial fibrillation (AF) significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography ( PPG) signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings. We propose a novel deep learning model, designed to learn how to retain accurate predictions from partially corrupted PPG.
  arXiv  Detail & Related papers  (2024-04-15T01:07:08Z)
• Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction [29.415616701032604]
  Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals. We propose a conditional score-based diffusion model to generate CTh trajectories with the given baseline information. Our model has a near-zero bias with narrow confidential 95% interval compared to the ground-truth CTh in 6-36 months.
  arXiv  Detail & Related papers  (2024-03-11T17:26:18Z)
• Early Warning Prediction with Automatic Labeling in Epilepsy Patients [4.6700203020828885]
  We propose a meta learning framework to improve the prediction of early ictal signals. The proposed bi-level optimization framework can help automatically label noisy data at the early ictal stage.
  arXiv  Detail & Related papers  (2023-10-09T18:12:46Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• 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)
• EventScore: An Automated Real-time Early Warning Score for Clinical Events [3.3039612529376625]
  We build an interpretable model for the early prediction of various adverse clinical events indicative of clinical deterioration. The model is evaluated on two datasets and four clinical events. Our model can be entirely automated without requiring any manually recorded features.
  arXiv  Detail & Related papers  (2021-02-11T11:55:08Z)

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.