PTB-XL, a large publicly available electrocardiography dataset
Patrick Wagner,Patrick Wagner,Patrick Wagner,Nils Strodthoff,Ralf-Dieter Bousseljot,Dieter Kreiseler,Fatima I Lunze,Wojciech Samek,Tobias Schaeffter,Tobias Schaeffter,Tobias Schaeffter +10 more
TLDR
PTB-XL is put forward, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length, which turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms.Abstract:
Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. The dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. The combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12098055read more
Citations
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Journal ArticleDOI
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020.
Erick A Perez Alday,Annie Gu,Amit J. Shah,Chad Robichaux,An-Kwok Ian Wong,Chengyu Liu,Feifei Liu,Ali Bahrami Rad,Andoni Elola,Andoni Elola,Salman Seyedi,Qiao Li,Ashish Sharma,Gari D. Clifford,Gari D. Clifford,Matthew A. Reyna +15 more
TL;DR: This work addresses issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020, setting a new bar in reproducibility for public data science competitions.
Journal ArticleDOI
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
TL;DR: First benchmarking results for the recently published, freely accessible clinical 12-lead ECG dataset PTB-XL are put forward, finding that convolutional neural networks, in particular resnet- and inception-based architectures, show the strongest performance across all tasks.
Journal ArticleDOI
Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram.
Yong-Yeon Jo,Young-Hoon Cho,Soo-Youn Lee,Joon-myoung Kwon,Kyung-Hee Kim,Ki-Hyun Jeon,Soohyun Cho,Jinsik Park,Byung Hee Oh +8 more
TL;DR: The results indicated that an explainable artificial intelligence methodology could be adopted to the DLM using ECG and enhance the transparency of theDLM for its application in clinical practice.
Journal ArticleDOI
Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram.
TL;DR: A deep neural network for automatic classification of cardiac arrhythmias from 12-lead ECG recordings showed superior performance than 4 machine learning methods learned from extracted expert features and employed the SHapley Additive exPlanations method.
References
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Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
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A study of cross-validation and bootstrap for accuracy estimation and model selection
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Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network
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