Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review

Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which...

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Autores:
Castillo García, Javier Ferney
Argüello-Prada, Erick Javier
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/16262
Acceso en línea:
https://hdl.handle.net/10614/16262
https://red.uao.edu.co/
Palabra clave:
Motion artifacts
Photoplethysmogram
Machine learning
Reference signal-less methods
Real-time applications
Computational complexity
Artefactos de movimiento
Fotopletismograma
Aprendizaje automático
Métodos sin señal de referencia
Aplicaciones en tiempo real
Complejidad computacional
Rights
openAccess
License
Derechos reservados - MDPI, 2025
id REPOUAO2_b08c07c5bc0bfa62bb3bb06df826deec
oai_identifier_str oai:red.uao.edu.co:10614/16262
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
dc.title.translated.spa.fl_str_mv Aprendizaje automático aplicado a la detección de artefactos de movimiento en señales fotopletismográficas sin señal de referencia: una revisión
title Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
spellingShingle Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
Motion artifacts
Photoplethysmogram
Machine learning
Reference signal-less methods
Real-time applications
Computational complexity
Artefactos de movimiento
Fotopletismograma
Aprendizaje automático
Métodos sin señal de referencia
Aplicaciones en tiempo real
Complejidad computacional
title_short Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
title_full Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
title_fullStr Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
title_full_unstemmed Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
title_sort Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review
dc.creator.fl_str_mv Castillo García, Javier Ferney
Argüello-Prada, Erick Javier
dc.contributor.author.none.fl_str_mv Castillo García, Javier Ferney
Argüello-Prada, Erick Javier
dc.subject.proposal.eng.fl_str_mv Motion artifacts
Photoplethysmogram
Machine learning
Reference signal-less methods
Real-time applications
Computational complexity
topic Motion artifacts
Photoplethysmogram
Machine learning
Reference signal-less methods
Real-time applications
Computational complexity
Artefactos de movimiento
Fotopletismograma
Aprendizaje automático
Métodos sin señal de referencia
Aplicaciones en tiempo real
Complejidad computacional
dc.subject.proposal.spa.fl_str_mv Artefactos de movimiento
Fotopletismograma
Aprendizaje automático
Métodos sin señal de referencia
Aplicaciones en tiempo real
Complejidad computacional
description Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which one is more suitable for a specific purpose. This narrative review examines the application of machine learning techniques for the reference signal-less detection of MAs in PPG signals. We did not consider articles introducing signal filtering or decomposition algorithms without previous identification of corrupted segments. Studies on MA-detecting approaches utilizing multiple channels and additional sensors such as accelerometers were also excluded. Despite its promising results, the literature on this topic shows several limitations and inconsistencies, particularly those regarding the model development and testing process and the measures used by authors to support the method’s suitability for real-time applications. Moreover, there is a need for broader exploration and validation across different body parts and a standardized set of experiments specifically designed to test and validate MA detection approaches. It is essential to provide enough elements to enable researchers and developers to objectively assess the reliability and applicability of these methods and, therefore, obtain the most out of them
publishDate 2024
dc.date.issued.none.fl_str_mv 2024
dc.date.accessioned.none.fl_str_mv 2025-08-13T16:35:12Z
dc.date.available.none.fl_str_mv 2025-08-13T16:35:12Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.eng.fl_str_mv Text
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.eng.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.spa.fl_str_mv Castillo García, J. F. y Argüello-Prada, E. J. (2024). Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review. 24(22). 17 p. https://doi.org/10.3390/s24227193
dc.identifier.issn.spa.fl_str_mv 14248220
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/16262
dc.identifier.doi.spa.fl_str_mv doi.org/10.3390/s24227193
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv https://red.uao.edu.co/
identifier_str_mv Castillo García, J. F. y Argüello-Prada, E. J. (2024). Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review. 24(22). 17 p. https://doi.org/10.3390/s24227193
14248220
doi.org/10.3390/s24227193
Universidad Autónoma de Occidente
Respositorio Educativo Digital UAO
url https://hdl.handle.net/10614/16262
https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 17
dc.relation.citationissue.spa.fl_str_mv 22
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 24
dc.relation.ispartofjournal.spa.fl_str_mv Sensor
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spelling Castillo García, Javier FerneyArgüello-Prada, Erick Javier2025-08-13T16:35:12Z2025-08-13T16:35:12Z2024Castillo García, J. F. y Argüello-Prada, E. J. (2024). Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A review. 24(22). 17 p. https://doi.org/10.3390/s2422719314248220https://hdl.handle.net/10614/16262doi.org/10.3390/s24227193Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which one is more suitable for a specific purpose. This narrative review examines the application of machine learning techniques for the reference signal-less detection of MAs in PPG signals. We did not consider articles introducing signal filtering or decomposition algorithms without previous identification of corrupted segments. Studies on MA-detecting approaches utilizing multiple channels and additional sensors such as accelerometers were also excluded. Despite its promising results, the literature on this topic shows several limitations and inconsistencies, particularly those regarding the model development and testing process and the measures used by authors to support the method’s suitability for real-time applications. Moreover, there is a need for broader exploration and validation across different body parts and a standardized set of experiments specifically designed to test and validate MA detection approaches. It is essential to provide enough elements to enable researchers and developers to objectively assess the reliability and applicability of these methods and, therefore, obtain the most out of themLos algoritmos de aprendizaje automático han aportado avances notables en la detección de artefactos de movimiento (MA) a partir del fotopletismograma (PPG) sin datos de referencia medidos o sintéticos. Sin embargo, ningún estudio ha aportado una síntesis de estos métodos, y mucho menos una discusión exhaustiva que ayude a decidir cuál es el más adecuado para un propósito específico. Esta revisión narrativa examina la aplicación de técnicas de aprendizaje automático para la detección de MA sin señal de referencia en señales PPG. No se consideraron artículos que introdujeran algoritmos de filtrado o descomposición de señales sin la identificación previa de segmentos corruptos. También se excluyeron estudios sobre enfoques de detección de MA que utilizan múltiples canales y sensores adicionales, como acelerómetros. A pesar de sus prometedores resultados, la literatura sobre este tema presenta varias limitaciones e inconsistencias, en particular las relacionadas con el proceso de desarrollo y prueba de modelos y las medidas utilizadas por los autores para respaldar la idoneidad del método para aplicaciones en tiempo real. Además, se necesita una exploración y validación más amplias en diferentes partes del cuerpo y un conjunto estandarizado de experimentos diseñados específicamente para probar y validar los enfoques de detección de MA. Es esencial proporcionar elementos suficientes para que los investigadores y desarrolladores puedan evaluar objetivamente la fiabilidad y aplicabilidad de estos métodos y, por tanto, obtener el máximo provecho de ellos17 páginasapplication/pdfengMDPIBasel, SwitzerlandDerechos reservados - MDPI, 2025https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Machine learning applied to reference signal-less detection of motion artifacts in photoplethysmographic signals: A reviewAprendizaje automático aplicado a la detección de artefactos de movimiento en señales fotopletismográficas sin señal de referencia: una revisiónArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a851722124Sensor1. 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