Hygeia: A Multilabel Deep Learning-Based Classification Method for Imbalanced Electrocardiogram Data

Xu, Xiaolong; Xu, Haifeng; Wang, Liying; Zhang, Yuanyuan; Xiao, Fu

doi:10.1109/tcbb.2022.3176905

IEEE/ACM Trans. Comput. Biol. and Bioinf.

2023

DOI: 10.1109/tcbb.2022.3176905

|View full text |Cite

Hygeia: A Multilabel Deep Learning-Based Classification Method for Imbalanced Electrocardiogram Data

Xiaolong Xu

Haifeng Xu

Liying Wang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…•Noise addition: The ECG signal x is modified by adding Gaussian random noise n. The noise n is generated by a random generator with a mean of 0 and a standard deviation of σ. Mathematically, the generated signal can be expressed as: x + n[19,[38][39][40][41][42][43][44][45]]. •Scaling: Each lead of the ECG signal is scaled by a random factor that is drawn from a normal distribution[21,25,[38][39][40]42,46].…”

mentioning

confidence: 99%

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

Rahman

Rivolta

Badilini

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

mentioning

confidence: 99%

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

Rahman

Rivolta

Badilini

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Compact Class-Conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification

Sáez,

Vera

2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

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

Wang,

Xie,

Yang

et al. 2024

Diagnostics

View full text Add to dashboard Cite

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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hygeia: A Multilabel Deep Learning-Based Classification Method for Imbalanced Electrocardiogram Data

Cited by 4 publications

References 37 publications

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

Compact Class-Conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification

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

Contact Info

Product

Resources

About