ECG-ViEW II, a freely accessible electrocardiogram database

Kim, Young-Gun; Shin, Dahye; Park, Man Young; Lee, Sukhoon; Jeon, Min Seok; Yoon, Dukyong; Park, Rae Woong

doi:10.1371/journal.pone.0176222

Cited by 22 publications

(21 citation statements)

References 21 publications

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“…The QTc data were extracted by parsing alphanumeric data from the PDF data extracted from the ECG repository using the web-scraping technique in our previous study. 12 This ECG database contains 1,040,752 ECG results from 447,632 patients as of January 2018 ( Figure 1).…”

Section: Data Sourcesmentioning

confidence: 99%

“…We have made an effort to collect portable ECG results from inpatients and outpatients for the last few years. 12 In this database, ECG parameters such as RR, PR, QRS, QT, and QTc intervals were extracted from raw ECG signals. The ECG database enabled us to conduct a study to provide direct evidence of QT prolongation caused by DDIs between hydroxychloroquine and other co-medications.…”

Section: Introductionmentioning

confidence: 99%

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Risk of QT Prolongation through Drug-drug Interactions between Hydroxychloroquine and Concomitant Drugs Prescribed in Real-world Practice

Choi

Koo

Kim

et al. 2020

Preprint

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Background: Hydroxychloroquine has recently received attention as a treatment for COVID-19. However, hydroxychloroquine may prolong the QTc interval, thus increasing the risk of life-threatening arrhythmia. Many patients with COVID-19 have comorbidities, necessitating the use of several drugs simultaneously with hydroxychloroquine. However, the risk of QT prolongation due to drug-drug interactions (DDIs) between hydroxychloroquine and these co-medications has not been identified. Therefore, it is necessary to investigate the risk of QT interval prolongation due to DDIs between hydroxychloroquine and frequently used concurrent drugs.Methods and Results: Using 447,632 patients and 1,040,752 electrocardiograms, we investigated the risk of QT prolongation due to DDIs between hydroxychloroquine and 118 concurrent drugs frequently used in real-world practice. In the analysis, we observed that 11 drugs (trimebutine, tacrolimus, tramadol, rosuvastatin, ciclosporin, sulfasalazine, rofecoxib, diltiazem, piperacillin/tazobactam, and isoniazid) show DDIs with hydroxychloroquine in the direction of QT prolongation.Conclusions: We found 11 drugs that show significant (p <0.05) DDIs with hydroxychloroquine, thereby increasing the risk of QT prolongation in patients. It is necessary to consider prescribing alternative drugs that have less DDI when these drugs are concurrently administered with hydroxychloroquine. Further investigation is needed to assess more profoundly the risk of QT prolongation due to DDI with hydroxychloroquine of each drug that we found in this analysis.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk of QT Prolongation through Drug-drug Interactions between Hydroxychloroquine and Concomitant Drugs Prescribed in Real-world Practice

Choi

Koo

Kim

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In this paper, we present a framework to analyze a comprehensive, longitudinal, and in the field of machine learning unexplored ECG database assembled from a 19year study period, ECG-ViEW II [14]. This database has provided a solution to the lack of well-annotated and large datasets for the training and validation of powerful but datahungry machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values

et al. 2020

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The early and accurate detection of the onset of acute myocardial infarction (AMI) is imperative for the timely provision of medical intervention and the reduction of its mortality rate. Machine learning techniques have demonstrated great potential in aiding disease diagnosis. In this paper, we present a framework to predict the onset of AMI using 713,447 extracted ECG samples and associated auxiliary data from the longitudinal and comprehensive ECG-ViEW II database, previously unexplored in the field of machine learning in healthcare. The framework is realized with two deep learning models, a convolutional neural network (CNN) and a recurrent neural network (RNN), and a decision-tree based model, XGBoost. Synthetic minority oversampling technique (SMOTE) was utilized to address class imbalance. High prediction accuracy of 89.9%, 84.6%, 97.5% and ROC curve areas of 90.7%, 82.9%, 96.5% have been achieved for the best CNN, RNN, and XGBoost models, respectively. Shapley values were utilized to identify the features that contributed most to the classification decision with XGBoost, demonstrating the high impact of auxiliary inputs such as age and sex. This paper demonstrates the promising application of explainable machine learning in the field of cardiovascular disease prediction. INDEX TERMS Machine learning, biomedical informatics, predictive models, acute myocardial infarction.

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“…Recent ECG-related research has required a substantially different approach to noise because the scale of data collection has become very large. The Electrocardiogram Vigilance with Electronic data Warehouse II (ECG-ViEW II) released 979,273 ECG results from 461,178 patients, with plans to add all 12-lead data in the next version [2223]. More recent attempts have, additionally, been made to acquire ECG measurements from patient monitoring equipment in intensive care units (ICUs) [2425].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model

et al. 2019

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Objectives Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert. Methods To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset. Results When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96). Conclusions The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.

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ECG-ViEW II, a freely accessible electrocardiogram database

Cited by 22 publications

References 21 publications

Risk of QT Prolongation through Drug-drug Interactions between Hydroxychloroquine and Concomitant Drugs Prescribed in Real-world Practice

Risk of QT Prolongation through Drug-drug Interactions between Hydroxychloroquine and Concomitant Drugs Prescribed in Real-world Practice

Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values

Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model

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