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

Saifur Rahman1, John Yearwood1, Chandan Karmakar2

  • 1School of Information Technology, Deakin University, Geelong, VIC, Australia.

Scientific Reports
|January 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new noise-presence framework for electrocardiograms (ECGs) that adapts filtering to detected noise, improving signal quality and preserving crucial cardiac measurements. This noise-adaptive filtering enhances diagnostic accuracy for ECG analysis.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Electrocardiograms (ECGs) are vital for cardiac monitoring but susceptible to noise, degrading signal quality.
  • Conventional ECG filtering methods apply uniform noise reduction, potentially distorting clean signal segments and important clinical features.

Purpose of the Study:

  • To develop and evaluate a novel noise-presence framework for ECG signal processing.
  • To adapt filtering strategies based on the presence and type of noise, aiming to minimize signal distortion and preserve clinically relevant ECG parameters.

Main Methods:

  • A noise-presence framework was developed to detect noise, classify its type, and apply tailored filtering.
  • Kernel density estimation (KDE) was used to assess the influence of sampling frequency on noise detection, identifying 500 Hz as optimal.
  • A hierarchical Adaboost model was implemented and compared against SVM, RF, and ExtraTree classifiers for noise detection and classification accuracy.

Main Results:

  • The hierarchical Adaboost model achieved high accuracy ([Formula: see text]) in noise detection and ([Formula: see text]) in noise classification across seven datasets.
  • Noise-profile filtering demonstrated superior performance, resulting in the smallest mean QT interval difference (2.50 ms) compared to noise-presence ([Formula: see text] ms) and noise-agnostic filtering ([Formula: see text] ms).
  • Significant improvements in QRS interval measurements were observed, reducing differences from [Formula: see text] ms (noise-agnostic) to [Formula: see text] ms (noise-presence) and 4.28 ms (noise-profile).

Conclusions:

  • Adapting ECG filtering based on noise presence and type significantly enhances the preservation of clinical parameters, leading to more reliable interval measurements.
  • The developed framework is suitable for portable ECG systems and can be extended to other physiological signals with appropriate retraining.
  • While trained on synthetic noise, the framework's practical applicability is supported by its effectiveness in preserving diagnostic ECG features.