Key result
Template-based Gaussian filtering outperforms standard Gaussian filtering for ECG denoising while preserving waveform fidelity.
Why the study?
Noise reduction in ECG preprocessing is critical for accurate diagnosis, and improved denoising methods with low computational complexity are needed.
A novel template-based one-dimensional Gaussian filter provides improved signal-to-noise ratio and waveform preservation for real-time ECG denoising, particularly suitable for resource-constrained environments.
May enhance real-time ECG denoising on constrained devices; leaves open prospective clinical validation.
Noise reduction is a critical step in electrocardiogram (ECG) preprocessing, as it directly affects the accuracy of subsequent diagnoses and analyses. This study proposes a template-based one-dimensional Gaussian filter derived from a two-dimensional spatial-domain Gaussian kernel using a structured down-sampling strategy. Unlike conventional one-dimensional Gaussian filters, the proposed method leverages the probabilistic structure of the Gaussian distribution to enhance denoising performance while maintaining low computational complexity. Experiments on synthetic signals with multiple noise types, together with ECG recordings from the MIT-BIH Arrhythmia Database and the MIT-BIH noise stress test database, demonstrate consistent improvements in signal-to-noise ratio (SNR) across a range of noise conditions. Comparative evaluations further show that, under identical kernel configurations, the proposed method achieves higher SNR values than MATLAB's built-in Gaussian filter while preserving waveform fidelity. Morphology-oriented metrics, including percent root-mean-square difference (PRD), correlation coefficients, and R-peak amplitude errors, together with paired-sample t-test analysis, confirm that the proposed filter effectively suppresses noise without introducing systematic amplitude bias or notable waveform distortion. These results indicate that the proposed approach offers a practical, lightweight, and reliable solution for real-time ECG denoising and other one-dimensional biomedical signal-processing applications, particularly in resource-constrained environments.
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Xia et al. (2026) studied ECG signal noise. Template-based one-dimensional Gaussian filter vs. MATLAB's built-in Gaussian filter was evaluated on Signal-to-noise ratio (SNR) and morphology-oriented metrics (PRD, correlation coefficients, R-peak amplitude errors). A proposed template-based one-dimensional Gaussian filter achieved higher signal-to-noise ratio values than MATLAB's built-in Gaussian filter for ECG denoising while preserving waveform fidelity.
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