ArticleFrontiers in cardiovascular medicine2024
A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification.Journal of imaging informatics in medicine · 2026Article
- Optimized Feature Selection and Deep Neural Networks to Improve Heart Disease Prediction.Journal of imaging informatics in medicine · 2026Article
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- GVMD-NLM: A Hybrid Denoising Method for GNSS Buoy Elevation Time Series Using Optimized VMD and Non-Local Means Filtering.Sensors (Basel, Switzerland) · 2026Article
- Comparative evaluation of filtration techniques for ECG signal denoising with emphasis on stationary wavelet transform.Scientific reports · 2025Article
- Deep Learning-Driven Early Diagnosis of Respiratory Diseases using CNN-RNN Fusion on Lung Sound Data.Scientific reports · 2025Article
- IoT assisted fetal health classification using mother optimization algorithm with deep learning approach on cardiotocogram data.Scientific reports · 2025Article
- Phospholipase C epsilon 1 as a therapeutic target in cardiovascular diseases.Journal of advanced research · 2025Review
- Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Revolutionizing AMD detection Bi model CNNs and hybrid feature selection for automated grading.Scientific reports · 2025Article
- STFT-based multisynchrosqueezing transform using a second-order signal model for seismic data analysis.Scientific reports · 2025Article
- Effectiveness of machine learning models in diagnosis of heart disease: a comparative study.Scientific reports · 2025Article
- Key factors in predictive analysis of cardiovascular risks in public health.Scientific reports · 2025Article
- Emerging rapid detection methods for the monitoring of cardiovascular diseases: Current trends and future perspectives.Materials today. Bio · 2025Review
- SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction.Scientific reports · 2025Article
- ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.BMC cardiovascular disorders · 2025Article
- Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.BMC infectious diseases · 2025Article
- Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder.Scientific reports · 2025Article
- Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms.Scientific reports · 2025Article
- Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective.BMC cardiovascular disorders · 2025Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis. Methods: In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity. Results: The proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD). Conclusions: The proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.
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