A Novel Method and Python Library for ECG Signal Quality Assessment.

Journal: Studies in health technology and informatics
Published Date: Aug 22, 2024

Abstract

Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG signal is very prone to being distorted through different sources of artifacts that can later interfere with the diagnostic. For this reason, signal quality assessment (SQA) methods that identify corrupted signals are critical to improve the robustness of automatic ECG diagnostic methods. This work presents a review and open-source implementation of different available indices for SQA as well as introducing an index that considers the ECG as a dynamical system. These indices are then used to develop machine learning models which evaluate the quality of the signals. The proposed index along the designed ML models are shown to improve SQA for ECG signals.

Authors

  • Charles Berger
    Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
  • Hugues Turbé
    Division of Medical Information Sciences, University Hospitals of Geneva.
  • Mina Bjelogrlic
    Division of Medical Information Sciences, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland.
  • Christian Lovis
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.

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