KennyVale/MEETI

Dataset details

About KennyVale/MEETI

Abstract Electrocardiograms (ECGs) are essential for diagnosing arrhythmias, myocardial ischemia, and conduction disorders. While machine learning has achieved expert-level performance in ECG interpretation, the development of clinically deployable multimodal aI systems is limited by the lack of public datasets that integrate raw signals, diagnostic images, and interpretation text. Most existing ECG datasets are single-modality or include, at most, signal-text pairs, restricting real-world applicability. To address this gap, we present MEETI (MIMIC-IV-Ext ECG-Text-Image), the first large-scale dataset that synchronizes raw ECG waveforms, high-resolution plotted images, and detailed textual interpretations generated by large language models. MEEtI also includes beat-level quantitative parameters extracted from each lead, enabling fine-grained analysis and improving model interpretability. Built on the MIMIC-IV-ECG database of over 800,000 recordings, MEETI aligns each record across four components using unique identifiers: (1) raw signals, (2) plotted images, (3) per-beat parameters, and (4) interpretation text. This structure enables multimodal transformer learning and supports explainable, integrated analysis. MEEtI provides a robust foundation and benchmark for next-generation cardiovascular artificial intelligence research. Background Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide [1], contributing to over 17 million deaths each year. Electrocardiography (ECG) remains the primary noninvasive modality for assessing cardiac electrophysiology