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A Novel Method and Python Library for ECG Signal Quality Assessment.
Berger, Charles; Turbé, Hugues; Bjelogrlic, Mina; Lovis, Christian.
Afiliación
  • Berger C; Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
  • Turbé H; Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
  • Bjelogrlic M; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
  • Lovis C; Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
Stud Health Technol Inform ; 316: 858-862, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39176928
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Electrocardiografía / Aprendizaje Automático Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Electrocardiografía / Aprendizaje Automático Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Países Bajos