In this paper, the UNNCyberHeart Database (UNNDB ) of ECG signals is presented for validation of ECG signals delineation algorithms. The database consists of 200  10-second 12-lead ECG signal records representing a wide class of different morphologies of the ECG signal . The boundaries of the ECG signal complexes were manually annotated by cardiologists for all 200 records independently for each lead.  In addition, all records have a complex diagnosis. With this database a new algorithm for delineation of the ECG signal was successfully tested, taking into account the set of ECG signal leads and automatically determining the morphologies of the complexes. Earlier this algorithm showed competitive accuracy on QTDB database, which has only 2 leads. All the advantages of the developed algorithm were completely revealed when analyzing the fully annotated 12-lead database presentedin this paper .
We report a new Lobachevsky University database (LUDB ) of ECG signals that contains 200 samples of 10-second 12-lead electrocardiograms (ECG) from different subjects . The boundaries of the ECG signal complexes are manually annotated by cardiologists for all samples and independently for each lead. The database is representative of a variety of signal morphologies. In addition, all records have an attributed diagnosis. 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 .
R
In this paper, the UNNCyberHeart Database (UNNDB
We report a new Lobachevsky University database (LUDB
R
is presented for validation of ECG signals delineation algorithms. The database consists of
that contains
A
samples of
R
ECG signal records representing a wide class of different morphologies of the ECG signal
electrocardiograms (ECG) from different subjects
R
were
are
R
200 records
samples and
A
The database is representative of a variety of signal morphologies.
R
a complex diagnosis. With this database a new algorithm for delineation of the ECG signal was successfully tested, taking into account the set of ECG signal leads and automatically determining the morphologies of the complexes. Earlier this algorithm showed competitive accuracy on QTDB database, which has only 2 leads. All the advantages of the developed algorithm were completely revealed when analyzing the fully annotated 12-lead database presentedin this paper
an attributed diagnosis. 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