PTB-XL+, a comprehensive electrocardiographic feature dataset
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PTB-XL+, a comprehensive electrocardiographic feature dataset. / Strodthoff, Nils; Mehari, Temesgen; Nagel, Claudia; Aston, Philip J.; Sundar, Ashish; Graff, Claus; Kanters, Jørgen K.; Haverkamp, Wilhelm; Dössel, Olaf; Loewe, Axel; Bär, Markus; Schaeffter, Tobias.
In: Scientific Data, Vol. 10, 279, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - PTB-XL+, a comprehensive electrocardiographic feature dataset
AU - Strodthoff, Nils
AU - Mehari, Temesgen
AU - Nagel, Claudia
AU - Aston, Philip J.
AU - Sundar, Ashish
AU - Graff, Claus
AU - Kanters, Jørgen K.
AU - Haverkamp, Wilhelm
AU - Dössel, Olaf
AU - Loewe, Axel
AU - Bär, Markus
AU - Schaeffter, Tobias
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists’ decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.
AB - Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists’ decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.
U2 - 10.1038/s41597-023-02153-8
DO - 10.1038/s41597-023-02153-8
M3 - Journal article
C2 - 37179420
AN - SCOPUS:85159149325
VL - 10
JO - Scientific data
JF - Scientific data
SN - 2052-4463
M1 - 279
ER -
ID: 370737193