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

arXiv: https://arxiv.org/abs/2406.16974 · HTML: https://mlsanity.com/paper/2406.16974/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)

## Built on by (most cited first, 2 of 2)

- [MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations](https://mlsanity.com/paper/2507.15255/) (2025)
- [REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality](https://mlsanity.com/paper/2607.06037/) (2026)

## Abstract (arXiv)

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.