4.6 Article

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals

Journal

FRONTIERS IN AGING NEUROSCIENCE
Volume 14, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2022.870844

Keywords

multiple-coefficient; transform domain; non-linear model; simulated annealing; dementia

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With the aging society, healthcare and elderly care have become the focus of medical care, especially for the elderly with dementia. This study proposes an intelligent healthcare system that hides patients' confidential data in electrocardiogram (ECG) signals. By optimizing the quality of the embedded ECG signals and using simulated annealing (SA), the embedded confidential data can satisfy the requirements of physiological diagnostics. Experimental results confirm the effectiveness of this method.
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.

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