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Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals
With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients’ confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069238/ https://www.ncbi.nlm.nih.gov/pubmed/35527738 http://dx.doi.org/10.3389/fnagi.2022.870844 |
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author | Zhao, Ming Chen, Shuo-Tsung Chen, Tzu-Li Tu, Shu-Yi Yeh, Cheng-Ta Lin, Fang-Yu Lu, Hao-Chun |
author_facet | Zhao, Ming Chen, Shuo-Tsung Chen, Tzu-Li Tu, Shu-Yi Yeh, Cheng-Ta Lin, Fang-Yu Lu, Hao-Chun |
author_sort | Zhao, Ming |
collection | PubMed |
description | With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients’ confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent healthcare system using the multiple-coefficient quantization technology in transform domain to hide patients’ confidential data into electrocardiogram (ECG) signals obtained by ECG sensor module. In embedding patients’ confidential data, we first consider a non-linear model for optimizing the quality of the embedded ECG signals. Next, we apply simulated annealing (SA) to solve the non-linear model so as to have good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE). Accordingly, the distortion of the PQRST complexes and the ECG amplitude is very small so that the embedded confidential data can satisfy the requirements of physiological diagnostics. In end devices, one can receive the ECG signals with the embedded confidential data and without the original ECG signals. Experimental results confirm the effectiveness of our method, which remains high quality for each ECG signal with the embedded confidential data no matter how the quantization size Q is increased. |
format | Online Article Text |
id | pubmed-9069238 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90692382022-05-05 Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals Zhao, Ming Chen, Shuo-Tsung Chen, Tzu-Li Tu, Shu-Yi Yeh, Cheng-Ta Lin, Fang-Yu Lu, Hao-Chun Front Aging Neurosci Neuroscience With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients’ confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent healthcare system using the multiple-coefficient quantization technology in transform domain to hide patients’ confidential data into electrocardiogram (ECG) signals obtained by ECG sensor module. In embedding patients’ confidential data, we first consider a non-linear model for optimizing the quality of the embedded ECG signals. Next, we apply simulated annealing (SA) to solve the non-linear model so as to have good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE). Accordingly, the distortion of the PQRST complexes and the ECG amplitude is very small so that the embedded confidential data can satisfy the requirements of physiological diagnostics. In end devices, one can receive the ECG signals with the embedded confidential data and without the original ECG signals. Experimental results confirm the effectiveness of our method, which remains high quality for each ECG signal with the embedded confidential data no matter how the quantization size Q is increased. Frontiers Media S.A. 2022-04-20 /pmc/articles/PMC9069238/ /pubmed/35527738 http://dx.doi.org/10.3389/fnagi.2022.870844 Text en Copyright © 2022 Zhao, Chen, Chen, Tu, Yeh, Lin and Lu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Zhao, Ming Chen, Shuo-Tsung Chen, Tzu-Li Tu, Shu-Yi Yeh, Cheng-Ta Lin, Fang-Yu Lu, Hao-Chun Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals |
title | Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals |
title_full | Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals |
title_fullStr | Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals |
title_full_unstemmed | Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals |
title_short | Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals |
title_sort | intelligent healthcare system using patients confidential data communication in electrocardiogram signals |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069238/ https://www.ncbi.nlm.nih.gov/pubmed/35527738 http://dx.doi.org/10.3389/fnagi.2022.870844 |
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