SHDB-AF: a Japanese Holter ECG database of atrial fibrillation

TL;DR

This paper introduces SHDB-AF, a comprehensive Japanese Holter ECG database of 100 patients with atrial fibrillation, aimed at improving ML and DL model robustness across diverse populations.

Contribution

The paper presents a novel, open-source Japanese ECG database with extensive 24-hour recordings, addressing the lack of ethnically diverse data for AF research.

Findings

Provides 24-hour ECG data from 100 Japanese AF patients

Enables development of more robust ML/DL models for diverse populations

Supports future research in AF diagnosis and management

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 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.