ArticleIEEE journal of biomedical and health informatics2024
DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal.
Article in IEEE journal of biomedical and health informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in machine and deep learning for ECG beat classification: a systematic review.Frontiers in digital health · 2025Pooled it
- Condition-fusion-and-hiding denoising diffusion model for electrocardiogram lead reconstruction.iScience · 2026Article
- KAN-DeScoD: Kolmogorov-Arnold Network Enhanced Deep Score-Based Diffusion Model for ECG Denoising.Sensors (Basel, Switzerland) · 2026Article
- Robust Design of Nonlinear Adaptive Hammerstein Filter Structure Using Evolutionary Algorithm: Real-Time Application to ECG Signals.Cardiovascular engineering and technology · 2026Article
- A Paralleled Multi-Task Learning-Based Framework for Single-Lead ECG Fine-Grained Noise Localization, Denoising and Signal Quality Assessment.Sensors (Basel, Switzerland) · 2025Article
- Review of diffusion models and its applications in biomedical informatics.BMC medical informatics and decision making · 2025Review
- Hybrid DWT NLM method with NOA optimization for ECG signal denoising.Scientific reports · 2025Article
- Physics-Inspired Diffusion Probabilistic Models for Improved Denoising in Intracardiac Time Series.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals.Sensors (Basel, Switzerland) · 2025Article
- A multi-feature dataset of coated end milling cutter tool wear whole life cycle.Scientific data · 2025Article
- Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review.Sensors (Basel, Switzerland) · 2024Review
- An error-bounded median filter for correcting ECG baseline wander.Health information science and systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveElectrocardiogram (ECG) signals commonly suffer noise interference, such as baseline wander. High-quality and high-fidelity reconstruction of the ECG signals is of great significance to diagnosing cardiovascular diseases. Therefore, this paper proposes a novel ECG baseline wander and noise removal technology.
methodsWe extended the diffusion model in a conditional manner that was specific to the ECG signals, namely the Deep Score-Based Diffusion model for Electrocardiogram baseline wander and noise removal (DeScoD-ECG). Moreover, we deployed a multi-shots averaging strategy that improved signal reconstructions. We conducted the experiments on the QT Database and the MIT-BIH Noise Stress Test Database to verify the feasibility of the proposed method. Baseline methods are adopted for comparison, including traditional digital filter-based and deep learning-based methods.
resultsThe quantities evaluation results show that the proposed method obtained outstanding performance on four distance-based similarity metrics with at least 20% overall improvement compared with the best baseline method.
conclusionThis paper demonstrates the state-of-the-art performance of the DeScoD-ECG for ECG baseline wander and noise removal, which has better approximations of the true data distribution and higher stability under extreme noise corruptions. SIGNIFICANCE: This study is one of the first to extend the conditional diffusion-based generative model for ECG noise removal, and the DeScoD-ECG has the potential to be widely used in biomedical applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.