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mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning
A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reco...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104941/ https://www.ncbi.nlm.nih.gov/pubmed/35590796 http://dx.doi.org/10.3390/s22093106 |
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author | Iyer, Srikrishna Zhao, Leo Mohan, Manoj Prabhakar Jimeno, Joe Siyal, Mohammed Yakoob Alphones, Arokiaswami Karim, Muhammad Faeyz |
author_facet | Iyer, Srikrishna Zhao, Leo Mohan, Manoj Prabhakar Jimeno, Joe Siyal, Mohammed Yakoob Alphones, Arokiaswami Karim, Muhammad Faeyz |
author_sort | Iyer, Srikrishna |
collection | PubMed |
description | A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R(2) value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%. |
format | Online Article Text |
id | pubmed-9104941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91049412022-05-14 mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning Iyer, Srikrishna Zhao, Leo Mohan, Manoj Prabhakar Jimeno, Joe Siyal, Mohammed Yakoob Alphones, Arokiaswami Karim, Muhammad Faeyz Sensors (Basel) Article A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R(2) value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%. MDPI 2022-04-19 /pmc/articles/PMC9104941/ /pubmed/35590796 http://dx.doi.org/10.3390/s22093106 Text en © 2022 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 Iyer, Srikrishna Zhao, Leo Mohan, Manoj Prabhakar Jimeno, Joe Siyal, Mohammed Yakoob Alphones, Arokiaswami Karim, Muhammad Faeyz mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning |
title | mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning |
title_full | mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning |
title_fullStr | mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning |
title_full_unstemmed | mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning |
title_short | mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning |
title_sort | mm-wave radar-based vital signs monitoring and arrhythmia detection using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104941/ https://www.ncbi.nlm.nih.gov/pubmed/35590796 http://dx.doi.org/10.3390/s22093106 |
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