LUDB: a new open-access validation tool for electrocardiogram delineation algorithms

Autor: Konstantin A. Kosonogov, Alena I. Kalyakulina, Alexander V. Nikolskiy, I. I. Yusipov, Nikolai Yu. Zolotykh, Victor A. Moskalenko, Mikhail Ivanchenko, Grigory V. Osipov
Rok vydání: 2018
Předmět:
Signal Processing (eess.SP)
General Computer Science
Computer science
0206 medical engineering
FOS: Physical sciences
02 engineering and technology
electrocardiogram
delineation algorithm
Quantitative Biology - Quantitative Methods
Database
QRS complex
Wavelet
T wave
0202 electrical engineering
electronic engineering
information engineering
medicine
FOS: Electrical engineering
electronic engineering
information engineering
General Materials Science
Electrical and Electronic Engineering
Electrical Engineering and Systems Science - Signal Processing
Quantitative Methods (q-bio.QM)
medicine.diagnostic_test
SIGNAL (programming language)
General Engineering
020601 biomedical engineering
Physics - Medical Physics
FOS: Biological sciences
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
Medical Physics (physics.med-ph)
Algorithm
Electrocardiography
lcsh:TK1-9971
Zdroj: IEEE Access, Vol 8, Pp 186181-186190 (2020)
DOI: 10.48550/arxiv.1809.03393
Popis: 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.
Comment: 11 pages, 10 figures, 6 tables
Databáze: OpenAIRE
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