Evidence map›Paper›PMID 39272695›Full record

ArticleDiagnostics (Basel, Switzerland)2024

Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis.

Liang-Hung Wang, Chao-Xin Xie, Tao Yang, Hong-Xin Tan, Ming-Hui Fan, I-Chun Kuo, Zne-Jung Lee, Tsung-Yi Chen, Pao-Cheng Huang, Shih-Lun Chen and 1 more

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2024. 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

11 authors.

Liang-Hung WangSchool of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China.ORCID 0000-0002-2115-8902
Chao-Xin XieThe Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Tao YangThe Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Hong-Xin TanThe Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Ming-Hui FanThe Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
I-Chun KuoCollege of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, China.
Zne-Jung LeeSchool of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China.
Tsung-Yi ChenDepartment of Electronic Engineering, Feng Chia University, Taichung 40724, Taiwan.
Pao-Cheng HuangDepartment of Electronic Engineering, National Cheng Kung University, Tainan 70101, Taiwan.
Shih-Lun ChenThe Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan.ORCID 0000-0002-4079-9350
Patricia Angela R AbuThe Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines.ORCID 0000-0002-8848-6644

Funding

This research was funded by the National Natural Science Foundation of China, and the Major Project and innovation platform of Science and Technology Agency of Fujian Province under Grant Nos. 61971140, 2020IM010200, and 2021H6003, 2021D036, 2022J01549, r 61971140, 2020IM010200, and 2021H6003, 2021D036, 2022J01549
6 · The paper itself

Abstract

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground-background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT-BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis.

Indexed as

ECG data recoveryECG signal extractionimage distortion correctionsignal reconstructionuneven light correction

Identifiers

PMID39272695
PMCPMC11394196

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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.