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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.
Affiliation
  • Zhao M; School of Computer Science, Yangtze University, Jingzhou, China.
  • Chen ST; Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan.
  • Chen TL; Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan.
  • Tu SY; Department of Mathematics, University of Michigan-Flint, Flint, MI, United States.
  • Yeh CT; Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan.
  • Lin FY; Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan.
  • Lu HC; Department of Cardiology, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Front Aging Neurosci ; 14: 870844, 2022.
Article de En | MEDLINE | ID: mdl-35527738
ABSTRACT
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.
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Front Aging Neurosci Année: 2022 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Front Aging Neurosci Année: 2022 Type de document: Article Pays d'affiliation: Chine