ArticleScientific data2025
SHDB-AF: a Japanese Holter ECG database of atrial fibrillation.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations.Scientific data · 2026Article
- Dynamic prediction of paroxysmal atrial fibrillation onset using longitudinal sample entropy in joint models.BMC medical research methodology · 2026Article
- Atrial Fibrillation and Atrial Flutter Detection Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- SHDB-AF: a Japanese Holter ECG database of atrial fibrillation.Scientific data · 2025Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is a common atrial arrhythmia that impairs quality of life and causes embolic stroke, heart failure and other complications. Recent advancements in machine learning (ML) and deep learning (DL) have shown potential for enhancing diagnostic accuracy. It is essential for DL models to be robust and generalizable across variations in ethnicity, age, sex, and other factors. Although a number of ECG database have been made available to the research community, none includes a Japanese population sample. Saitama Heart Database Atrial Fibrillation (SHDB-AF) is a novel open-sourced Holter ECG database from Japan, containing 128 ECG with detailed clinical information from 122 unique patients. Each record in SHDB-AF is 24 hours long and has two channels, totaling 21.6 million seconds of ECG data. The dataset is available at https://physionet.org/content/shdb-af/ .
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Registered trials
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