Design and evaluation of a knowledge-based ECG noise filtering framework

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.