EchoNext: A Dataset for Detecting Echocardiogram-Confirmed Structural Heart Disease from ECGs

This dataset contains a de-identified collection of **100,000** 12-lead
electrocardiograms (ECGs) with paired structural heart disease (SHD) labels
derived from echocardiography, collected at Columbia University Irving Medical
Center. Each ECG is provided with raw waveform data sampled at 250 Hz across
all 12 leads, along with accompanying demographic and ECG-specific tabular
metadata, including age, sex, heart rate, PR interval, QRS duration, and
corrected QT interval. Each ECG is annotated with a binary label indicating
the presence or absence of structural heart disease based on echocardiographic
findings, including reduced left ventricular ejection fraction, increased
ventricular wall thickness, significant valvular disease, right ventricular
dysfunction, pulmonary hypertension, or pericardial effusion.

This dataset was developed as part of the creation of the **Columbia Mini-
Model** , a lightweight deep learning model for SHD detection from ECGs. The
dataset represents a simplified, focused subset of the larger EchoNext
training population and was used to evaluate model performance in resource-
constrained settings or smaller-scale deployment environments. It is being
released to promote transparency and reproducibility, support further research
in cardiovascular AI, and enable benchmarking of lightweight ECG-based
screening models for structural heart disease.