Evidence map›Paper›PMID 35527738›Full record

ArticleFrontiers in aging neuroscience2022

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals.

Ming Zhao, Shuo-Tsung Chen, Tzu-Li Chen, Shu-Yi Tu, Cheng-Ta Yeh, Fang-Yu Lin, Hao-Chun Lu

Abstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ming ZhaoSchool of Computer Science, Yangtze University, Jingzhou, China.
Shuo-Tsung ChenDepartment of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan.
Tzu-Li ChenDepartment of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan.
Shu-Yi TuDepartment of Mathematics, University of Michigan-Flint, Flint, MI, United States.
Cheng-Ta YehDepartment of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan.
Fang-Yu LinGraduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan.
Hao-Chun LuDepartment of Cardiology, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

dementiamultiple-coefficientnon-linear modelsimulated annealingtransform domain

Identifiers

PMID35527738
PMCPMC9069238

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.