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Leipzig heart center ECG-database: arrhythmias in children and patients with congenital heart disease

Sophia Klehs1, Daniel Franke2, Bayhas Alhamad1

  • 1Department for Pediatric Cardiology, University of Leipzig-Heart Center, Leipzig, Germany.

Physiological Measurement
|July 24, 2026
PubMed

Insights

This study introduces a new, open-source electrocardiogram (ECG) database for children and adults with congenital heart defects (CHD). The annotated dataset aids in developing algorithms for detecting cardiac arrhythmias in this patient group.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Medical Informatics

Background:

  • Electrocardiogram (ECG) interpretation is increasingly supported by algorithmic analysis.
  • Large datasets are crucial for developing and validating these algorithms.
  • A gap exists in manually annotated, open-source ECG databases for pediatric and adult congenital heart defect (CHD) populations.

Purpose of the Study:

  • To present the first manually annotated, open-source ECG database specifically for individuals with congenital heart defects (CHD).
  • To provide a foundational dataset for the development of ECG analysis algorithms tailored to this unique patient group.

Main Methods:

  • Raw ECG and intracardiac electrogram data were collected from pediatric and adult CHD patients undergoing electrophysiological studies (EPS).
  • Data was formatted using the WaveForm Database (WFDB) standard.
  • Annotations of cardiac rhythms and beats were performed manually using LightWAVE® software.

Main Results:

  • The database comprises 39 ECGs from 39 patients, totaling 113,924 annotated beats.
  • Key findings include normal sinus rhythm (33%), supraventricular tachycardias (23%) including AVRT and AVNRT, and various bundle branch blocks.
  • The dataset also features paced beats (12%), premature beats (7%), and rarer arrhythmias like aberrated AVRT/AVNRT and heart blocks.

Conclusions:

  • This open-source ECG database offers a valuable resource for advancing ECG interpretation in CHD patients.
  • It facilitates the development and validation of specialized algorithms for detecting cardiac pathologies in this population.
  • The comprehensive annotations provide a solid foundation for future research in cardiac electrophysiology.

Keywords:
AVNRTAVRTECG-databaseartificial intelligencecongenital heart diseasesupraventricular tachycardia

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