CODE-II: a large-scale dataset for artificial intelligence in ECG analysis.

Petrus E O G B Abreu¹, Gabriela M M Paixão², Jiawei Li³, Paulo R Gomes⁴, Peter W Macfarlane⁵, Ana C S Oliveira², Vinícius T Carvalho², Thomas B Schön³, Antonio Luiz P Ribeiro^6,7, Antônio H Ribeiro⁸

¹Faculdade de Medicina, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil. petrusabreu@ufmg.br.

²Faculdade de Medicina, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil.

³Uppsala University, Uppsala, Sweden.

⁴Telehealth Center, Hospital das Clínicas, UFMG, Belo Horizonte, Brazil.

⁵University of Glasgow, Glasgow, UK.

⁶Faculdade de Medicina, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil. antonio.ribeiro@ebserh.gov.br.

⁷Telehealth Center, Hospital das Clínicas, UFMG, Belo Horizonte, Brazil. antonio.ribeiro@ebserh.gov.br.

⁸Uppsala University, Uppsala, Sweden. antonio.horta.ribeiro@it.uu.se.

PubMed DOI

Abstract

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligence (AI) based ECG analysis, yet limitations in annotation quality, size, and scope remain major challenges. Here we present CODE-II, a large-scale real-world dataset of 2,735,269 12-lead ECGs from 2,093,807 adult patients collected by the Telehealth N…

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