Evidence map›Paper›PMID 38005585›Full record

ArticleSensors (Basel, Switzerland)2023

Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals.

Shuo-Tsung Chen, Ren-Jie Ye, Tsung-Hsien Wu, Chun-Wen Cheng, Po-You Zhan, Kuan-Ming Chen, Wan-Yu Zhong

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Shuo-Tsung ChenDepartment of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.
Ren-Jie YeGraduate School of Applied Chinese Studies, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.
Tsung-Hsien WuBachelor's Program in Business Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Chun-Wen ChengDepartment of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.
Po-You ZhanDepartment of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.
Kuan-Ming ChenDepartment of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.
Wan-Yu ZhongDepartment of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients' information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients' information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30.

Indexed as

ElectrocardiographySignal Processing, Computer-AssistedAlgorithmsHumansSignal-To-Noise RatioamplitudeECGhiding statenon-linearPSOswitchtransform domain

Identifiers

PMID38005585
PMCPMC10675253

What OpenQuestion holds

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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.