Cargando…

MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise....

Descripción completa

Detalles Bibliográficos
Autores principales: Xiang, Yashan, Guo, Jian, Chen, Ming, Wang, Zheyu, Han, Chong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535668/
https://www.ncbi.nlm.nih.gov/pubmed/37765926
http://dx.doi.org/10.3390/s23187869
_version_ 1785112685289930752
author Xiang, Yashan
Guo, Jian
Chen, Ming
Wang, Zheyu
Han, Chong
author_facet Xiang, Yashan
Guo, Jian
Chen, Ming
Wang, Zheyu
Han, Chong
author_sort Xiang, Yashan
collection PubMed
description Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the “masked phase autoencoders with a vision transformer network” (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time.
format Online
Article
Text
id pubmed-10535668
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-105356682023-09-29 MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals Xiang, Yashan Guo, Jian Chen, Ming Wang, Zheyu Han, Chong Sensors (Basel) Article Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the “masked phase autoencoders with a vision transformer network” (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. MDPI 2023-09-13 /pmc/articles/PMC10535668/ /pubmed/37765926 http://dx.doi.org/10.3390/s23187869 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
Xiang, Yashan
Guo, Jian
Chen, Ming
Wang, Zheyu
Han, Chong
MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals
title MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals
title_full MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals
title_fullStr MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals
title_full_unstemmed MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals
title_short MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals
title_sort mae-based self-supervised pretraining algorithm for heart rate estimation of radar signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535668/
https://www.ncbi.nlm.nih.gov/pubmed/37765926
http://dx.doi.org/10.3390/s23187869
work_keys_str_mv AT xiangyashan maebasedselfsupervisedpretrainingalgorithmforheartrateestimationofradarsignals
AT guojian maebasedselfsupervisedpretrainingalgorithmforheartrateestimationofradarsignals
AT chenming maebasedselfsupervisedpretrainingalgorithmforheartrateestimationofradarsignals
AT wangzheyu maebasedselfsupervisedpretrainingalgorithmforheartrateestimationofradarsignals
AT hanchong maebasedselfsupervisedpretrainingalgorithmforheartrateestimationofradarsignals