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Deep learning for digitizing highly noisy paper-based ECG records
- Source :
- Computers in biology and medicine. 127
- Publication Year :
- 2020
-
Abstract
- Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the paper-based ECG records into a high-quality signal is critical for further analysis. We formulated the digitization problem as a segmentation problem and proposed a deep learning method to digitize highly noisy ECG scans. Our method extracts the ECG signal in an end-to-end manner and can handle different paper record layouts. In the experiment, our model clearly extracted the ECG waveform with a Dice coefficient of 0.85 and accurately measured the common ECG parameters with more than 0.90 Pearson's correlation. We showed that the end-to-end approach with deep learning can be powerful in ECG digitization. To the best of our knowledge, we provide the first approach to digitize the least informative noisy binary ECG scans and potentially be generalized to digitize various ECG records.
- Subjects :
- 0301 basic medicine
Computer science
Health Informatics
Data_CODINGANDINFORMATIONTHEORY
Signal
03 medical and health sciences
Electrocardiography
0302 clinical medicine
Deep Learning
Sørensen–Dice coefficient
Waveform
ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS
Segmentation
Digitization
Signal processing
business.industry
Deep learning
Pattern recognition
Signal Processing, Computer-Assisted
Image segmentation
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
030104 developmental biology
Artificial intelligence
business
030217 neurology & neurosurgery
Algorithms
Subjects
Details
- ISSN :
- 18790534
- Volume :
- 127
- Database :
- OpenAIRE
- Journal :
- Computers in biology and medicine
- Accession number :
- edsair.doi.dedup.....d8b2003ed17ef63827e896a13b5c8a29