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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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7193 |
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| author | Erick Javier Argüello-Prada Javier Ferney Castillo García |
| author_facet | Erick Javier Argüello-Prada Javier Ferney Castillo García |
| author_sort | Erick Javier Argüello-Prada |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-8d1d1c1d3a5d44a6a8b26855e3a1ea5a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-8d1d1c1d3a5d44a6a8b26855e3a1ea5a2024-11-26T18:21:03ZengMDPI AGSensors1424-82202024-11-012422719310.3390/s24227193Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A ReviewErick Javier Argüello-Prada0Javier Ferney Castillo García1Programa de Bioingeniería, Facultad de Ingeniería, Universidad Santiago de Cali, Calle 5 # 62-00 Barrio Pampalinda, Santiago de Cali 760032, ColombiaPrograma de Mecatrónica, Facultad de Ingeniería, Universidad Autónoma de Occidente, Calle 25 # 115-85 Vía Cali-Jamundí, Santiago de Cali 760030, ColombiaMachine 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.https://www.mdpi.com/1424-8220/24/22/7193motion artifactsphotoplethysmogrammachine learningreference signal-less methodsreal-time applicationscomputational complexity |
| spellingShingle | Erick Javier Argüello-Prada Javier Ferney Castillo García Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review Sensors motion artifacts photoplethysmogram machine learning reference signal-less methods real-time applications computational complexity |
| title | 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_short | 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 |
| topic | motion artifacts photoplethysmogram machine learning reference signal-less methods real-time applications computational complexity |
| url | https://www.mdpi.com/1424-8220/24/22/7193 |
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