1
|
Wang LH, Xie CX, Yang T, Tan HX, Fan MH, Kuo IC, Lee ZJ, Chen TY, Huang PC, Chen SL, Abu PAR. Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis. Diagnostics (Basel) 2024; 14:1910. [PMID: 39272695 PMCID: PMC11394196 DOI: 10.3390/diagnostics14171910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/24/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
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.
Collapse
Grants
- 61971140, 2020IM010200, and 2021H6003, 2021D036, 2022J01549 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
Collapse
Affiliation(s)
- Liang-Hung Wang
- School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Chao-Xin Xie
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Tao Yang
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Hong-Xin Tan
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Ming-Hui Fan
- The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - I-Chun Kuo
- College of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, China
| | - Zne-Jung Lee
- School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China
| | - Tsung-Yi Chen
- Department of Electronic Engineering, Feng Chia University, Taichung 40724, Taiwan
| | - Pao-Cheng Huang
- Department of Electronic Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shih-Lun Chen
- The Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan
| | - Patricia Angela R Abu
- The Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines
| |
Collapse
|
2
|
Rahman MM, Rivolta MW, Badilini F, Sassi R. A Systematic Survey of Data Augmentation of ECG Signals for AI Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:5237. [PMID: 37299964 PMCID: PMC10256074 DOI: 10.3390/s23115237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023]
Abstract
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
Collapse
Affiliation(s)
- Md Moklesur Rahman
- Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
| | | | - Fabio Badilini
- School of Nursing, University of California, San Francisco, CA 94143, USA
- AMPS-LLC, New York, NY 10025, USA
| | - Roberto Sassi
- Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
| |
Collapse
|