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 data from 100 unique patients with paroxysmal AF. Each record in SHDB-AF is 24 hours long and sampled at 200 Hz, totaling 24 million seconds of ECG data.

心房颤动是一种常见的心房心律失常，通过机器学习和深度学习的进展，可以提高诊断准确性；然而，要求深度学习模型在种族、年龄、性别和其他因素变化时具有稳健性和可推广性，而现有的心电图数据库中没有包括日本人群的样本。本研究提供了一个新的开源心电图数据库，其中包含了100个患有阵发性心房颤动的日本患者的数据。该数据库为时长24小时，采样频率为200赫兹，共包含2400万秒的心电图数据。

SHDB-AF：一份包含心房颤动的日本Holter心电数据库