LabelECG: A Web-Based Tool for Distributed Electrocardiogram Annotation

Ding, Zijian; Qiu, Shan; Guo, Yutong; Lin, Jianping; Sun, Lingyun; Fu, Dapeng; Yang, Zhen; Li, Chengquan; Yu, Yang; Meng, Long; Lv, Tingting; Li, Dan; Zhang, Ping

doi:10.1007/978-3-030-33327-0_13

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The workflow of annotating and reviewing is the same as in clinical practice, with one technician annotating an ECG record, and one doctor reviewing this record. The experts utilized a webbased tool for distributed ECG annotation in a local area network [7]. The dataset building has been approved by the ethics committees of the four hospitals.…”

Section: Dataset Buildingmentioning

confidence: 99%

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram

Ding

Wang

Yang

et al. 2021

Med Biol Eng Comput

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Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods.

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Section: Dataset Buildingmentioning

confidence: 99%

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram

Ding

Wang

Yang

et al. 2021

Med Biol Eng Comput

Self Cite

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“…web-based tools to visualize and annotate ECG signals have not reduced the annotation time and effort significantly. For example, it took 4 doctors, almost 3 months to annotate 15, 000 short ECG records using the LabelECG tool 5 . In general, gold standard expert annotations can be costly.…”

Section: Introductionmentioning

confidence: 99%

Weak Supervision for Affordable Modeling of Electrocardiogram Data

Goswami¹,

Boecking²,

Dubrawski³

2022

Preprint

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Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. While collecting vast amounts of unlabeled data can be straightforward, the point-by-point annotation of abnormal heartbeats is tedious and expensive. We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points. Our work is among the first to define weak supervision sources directly on time series data. Results show that with as few as six intuitive time series heuristics, we are able to infer high quality probabilistic label estimates for over 100,000 heartbeats with little human effort, and use the estimated labels to train competitive classifiers evaluated on held out test data.

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“…Prior annotation tools, such as MetaAnn [1] and WAVE [2], focused on a single-user mode annotation in a local desk-top setting without support for remote Web-based access. Web-based EKG annotators such as WaveformECG [3] and LabelECG [4] were developed to enable remote visualization and annotation of ECG waveforms.…”

Section: Introductionmentioning

confidence: 99%

PhysioTag: An Open-Source Platform for Collaborative Annotation of Physiological Waveforms

McCullum¹,

Hasan²,

Moody³

et al. 2022

Computing in Cardiology Conference (CinC)

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To develop robust algorithms for automated diagnosis of medical conditions such as cardiac arrhythmias, researchers require large collections of data with human expert annotations. Currently, there is a lack of accessible, open-source platforms for human experts to collaboratively develop these annotated datasets through a web interface. In this work, we developed a flexible, generalizable, web-based framework to enable multiple users to create and share annotations on multi-channel physiological waveforms. Using the developed annotation platform, we carried out a pilot study to assess the validity of ventricular tachycardia (VT) alarms from multiple commercial monitors. Thus far, four clinical experts have used this annotation tool to annotate a total of 5,658 VT alarm events, among which approximately 44% (N=2,468) have been labeled by two independent annotators.

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LabelECG: A Web-Based Tool for Distributed Electrocardiogram Annotation

Cited by 6 publications

References 10 publications

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram

Weak Supervision for Affordable Modeling of Electrocardiogram Data

PhysioTag: An Open-Source Platform for Collaborative Annotation of Physiological Waveforms

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