Key result
Noise-profile filtering outperforms agnostic and presence-based filtering, preserving ECG features with ~2.5 ms QT difference.
Why the study?
Conventional ECG preprocessing applies identical filters regardless of noise level, which can distort clean signal segments.
Does a knowledge-based ECG noise filtering framework improve the preservation of clinical ECG parameters (QT and QRS intervals) compared to noise-agnostic filtering?
Does a knowledge-based ECG noise filtering framework improve the preservation of clinical ECG parameters (QT and QRS intervals) compared to noise-agnostic filtering?
Adapting ECG filtering strategies based on the presence and specific type of noise significantly reduces the distortion of critical clinical parameters like QT and QRS intervals compared to conventional noise-agnostic filtering.
May warrant testing of noise-profile filtering in clinical ECGs; hypothesis-generating from animal data, human validation needed.
Electrocardiograms (ECGs) are widely used for cardiac monitoring but are often affected by noise that degrades signal quality. Conventional preprocessing applies the same filters regardless of noise level, which can distort clean segments. We introduce a noise-presence framework that identifies whether noise is present, determines its type and then applies filtering suited to the specific contamination. This approach aims to reduce unnecessary distortion and preserve clinically important features. We evaluate the framework by measuring changes in QT and QRS intervals under noise-agnostic, noise-presence and noise-profile filtering. We also examine how sampling frequency influences noise detection and classification using kernel density estimation (KDE) and find that 500 Hz offers the best performance. A hierarchical Adaboost model outperforms support vector machine (SVM), random forest (RF), and ExtraTree classifiers, reaching [Formula: see text] accuracy in noise detection and [Formula: see text] in noise classification across seven datasets. Noise-profile filtering achieves the smallest mean QT difference at 2.50 ms compared with noise-presence at [Formula: see text] ms and noise-agnostic filtering at [Formula: see text] ms. QRS differences improve from [Formula: see text] ms with noise-agnostic filtering to [Formula: see text] ms with noise-presence and 4.28 ms with noise-profile filtering. The results show that adapting the filtering strategy to noise presence and type offers clear advantages in preserving clinical ECG parameters, which supports more reliable interval measurements in diagnostic settings. The main limitation is that the model is trained with synthetic noise, which may not capture the full range of real-world artefacts. This limitation remains, but the framework is still suitable for portable ECG systems and can be extended to other physiological signals by retraining on data from the target modality. The results indicate that adapting the filtering strategy to noise presence and type provides clear benefits in preserving clinical ECG parameters, supporting more reliable interval measurements in diagnostic settings. While the model was trained using synthetic noise, which may not fully represent all real-world artefacts, this does not diminish its practical applicability. The framework remains well-suited for portable ECG systems and can be extended to other physiological signals by retraining on data from the target modality.
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Rahman et al. (2026) studied this question. Noise-profile filtering achieved the smallest mean QT difference of 2.50 ms, outperforming noise-presence and noise-agnostic filtering in preserving clinical ECG features.
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