Electrocardiography (ECG) is nowadays still the most available and widely used method for the cardiovascular system examination [1]. ECG signal reflects the electrical activity of the heart and provides a significant amount of information about the heart function [2]. Accurate detection of ECG components, such as P wave, QRS complex and T wave are fundamental steps of ECG analysis and subsequent cardiac pathological events detection. In practice, automated evaluation of ECG records using software is necessary [3]. The detection of QRS complexes and T waves is usually efficient. However, methods for P wave detection are not so successful in physiological signals and especially in pathological signals. It applies both in real practise and research [4],[5],[6],[7].
One of the reasons that prevents the progress in this field is a lack of publicly available datasets with correct P waves annotations suitable for training and testing of detection algorithms [5],[8].
The methods are usually tested only on a part of publicly available QT database [9],[10] or on a not publicly available CSE database [11], both with manual P waves annotations. In addition to this, there are two new databases, namely MIT-BIH Arrhythmia Database P-Wave Annotations [5],[9],[12] and Lobachevsky University Electrocardiography Database [9],[6], whose use is not frequent yet. There are also two publicly available databases with P waves annotations which contain mistakes - P waves annotations of MIT-BIH Arrhythmia database by Elgendi et al. [13] and automatically annotated part of QT database [10]. Thus, these annotations cannot be recommended to be used for testing of P wave detection algorithms.
The most commonly used databases, QT database and CSE database contain predominantly physiological ECG records, or contain only those pathologies which do not affect P wave detection. However, the content of the pathologies in databases is very important for objective testing of P wave detection algorithms. During the pathological function of the heart, the information about the positions of the P waves is very important for determining the diagnosis. Unfortunately, current algorithms are not able to detect P waves in pathological signals reliably.
Therefore, we fill this gap and introduce a new database of ECG signals with manually annotated P waves. The Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB) database consists of 50 2-minute 2-lead ECG signals with 23 types of pathologies. The P waves positions were manually annotated by two ECG experts with 7 years of practical experiences with evaluation of holter ECG records in cardiovascular ambulance [5]. The database will help to develop new, more accurate, and robust methods for processing and analysing ECG records in the sense of P wave detection.