Safeguarding Patient Privacy in ECG Signals with Intelligent Healthcare System

Published on April 21, 2022

Just like protecting the privacy of our personal information is crucial, safeguarding the confidential data of patients is equally important in healthcare. A team of researchers has developed an intelligent healthcare system that utilizes cutting-edge technology to hide patients’ confidential data within electrocardiogram (ECG) signals. They employ a non-linear model and simulated annealing to optimize the quality of the hidden data, ensuring minimal distortion in the ECG signals and meeting the requirements of physiological diagnostics. The results of the experiments are promising, demonstrating the effectiveness of this method. If you’re interested in learning more about this innovative approach to maintaining patient privacy in healthcare, dive into the underlying research!

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.

Read Full Article (External Site)

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes:

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>