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Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification

Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), provi...

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Detalles Bibliográficos
Autores principales: Cosoli, Gloria, Antognoli, Luca, Scalise, Lorenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953541/
https://www.ncbi.nlm.nih.gov/pubmed/36831919
http://dx.doi.org/10.3390/bios13020154
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author Cosoli, Gloria
Antognoli, Luca
Scalise, Lorenzo
author_facet Cosoli, Gloria
Antognoli, Luca
Scalise, Lorenzo
author_sort Cosoli, Gloria
collection PubMed
description Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called “white coat effect”. Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of −1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view.
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spelling pubmed-99535412023-02-25 Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification Cosoli, Gloria Antognoli, Luca Scalise, Lorenzo Biosensors (Basel) Article Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called “white coat effect”. Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of −1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view. MDPI 2023-01-18 /pmc/articles/PMC9953541/ /pubmed/36831919 http://dx.doi.org/10.3390/bios13020154 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cosoli, Gloria
Antognoli, Luca
Scalise, Lorenzo
Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification
title Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification
title_full Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification
title_fullStr Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification
title_full_unstemmed Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification
title_short Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification
title_sort wearable electrocardiography for physical activity monitoring: definition of validation protocol and automatic classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953541/
https://www.ncbi.nlm.nih.gov/pubmed/36831919
http://dx.doi.org/10.3390/bios13020154
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