We report Lobachevsky University Database (LUDB) of ECG signals that contains 200 recordings of 10-second 12-lead electrocardiograms (ECG) from different subjects  . The boundaries and peaks of QRS complexes and P and T waves are manually annotated by cardiologists for all recordings and independently for each lead, and all records received an expert classification by abnormalities. In addition, the database is representative of a variety of signal morphologies. These features make LUDB a promising tool for validating ECG delineation algorithms across a broad range of ECG signal shapes and patient diagnoses. A case study for the recently proposed wavelet-based algorithm is presented .
We report Lobachevsky University Database (LUDB) of ECG signals , an open tool for validating ECG delineation algorithms, that is superior to the existing publicly available data bases in several aspects. LUDB contains 200 recordings of 10-second 12-lead electrocardiograms (ECG) from different subjects , representative of a variety of signal morphologies . The boundaries and peaks of QRS complexes and P and T waves are manually annotated by cardiologists for all recordings and independently for each lead, and all records received an expert classification by abnormalities. We present a case study for the recently proposed wavelet-based algorithm and the broadly used ecg-kit tool, and demonstrate the advantage of multi-lead ECG data analysis. LUDB contributes to the diversity of public databases employed in developing and validating novel ECG analysis algorithms, including the most advanced based on deep learning neural networks .
R
that
, an open tool for validating ECG delineation algorithms, that is superior to the existing publicly available data bases in several aspects. LUDB
A
, representative of a variety of signal morphologies
R
In addition, the database is representative of a variety of signal morphologies. These features make LUDB a promising tool for validating ECG delineation algorithms across a broad range of ECG signal shapes and patient diagnoses. A
We present a
R
is presented
and the broadly used ecg-kit tool, and demonstrate the advantage of multi-lead ECG data analysis. LUDB contributes to the diversity of public databases employed in developing and validating novel ECG analysis algorithms, including the most advanced based on deep learning neural networks