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

TL;DR

LUDB is an open-access ECG database with 200 multi-lead recordings, manually annotated by cardiologists, designed to improve validation and development of ECG delineation algorithms, especially for deep learning applications.

Contribution

This paper introduces LUDB, a comprehensive, manually annotated ECG database that enhances existing resources for algorithm validation and development.

Findings

LUDB outperforms existing databases in diversity and annotation quality.

Multi-lead ECG analysis shows advantages over single-lead approaches.

Demonstrated utility with wavelet-based and ecg-kit algorithms.

Abstract

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.