Key result
The BUT PDB was created, comprising 50 two-minute pathological ECG records with manually annotated P waves to facilitate the development of P wave detection algorithms.
Why the study?
Few databases of pathological ECG signals with P-wave annotations exist, and some are incorrect, limiting the development, evaluation, and objective comparison of P-wave detection algorithms.
The creation of the BUT PDB provides a new, expertly annotated database of pathological ECG signals to facilitate the development and evaluation of P wave detection algorithms.
May aid P-wave algorithm development in arrhythmia evaluation; leaves open prospective clinical validation before practice impact.
Objective. The aim of this study is to create a database for the development, evaluation and objective comparison of algorithms for P wave detection in ECG signals. B rno U niversity of T echnology ECG Signal D ata b ase with Annotations of P- Wave (BUT PDB) is an ECG signal database with marked peaks of P waves annotated by ECG experts. Currently, there are only a few databases of pathological ECG signals with P-wave annotations, and some are incorrect. Approach. The pathological ECG signals used in this work were selected from three existing databases of ECG signals: MIT-BIH Arrhythmia Database, MIT-BIH Supraventricular Arrhythmia Database and Long Term AF Database. The P-wave positions were manually annotated by two ECG experts in all selected signals. Main results. The final BUT PDB composed of selected signals consists of 50 two-minute, two-lead pathological ECG signal records with annotated P waves. Each record also contains a description of the diagnosis (pathology) present in the selected part of the record and information about positions and types of QRS complexes. Significance. The BUT PDB is created for developing new, more accurate and robust methods for P wave detection. These algorithms will be used in medical practice and will help cardiologists to evaluate ECG records, establish diagnoses and save time.
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Saclova et al. (2022) studied Arrhythmias (n=50). The BUT PDB was created, comprising 50 two-minute pathological ECG records with manually annotated P waves to facilitate the development of P wave detection algorithms.
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