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

Kenta Tsutsui; Shany Biton Brimer; Noam Ben-Moshe; Jean Marc Sellal; Julien Oster; Hitoshi Mori; Yoshifumi Ikeda; Takahide Arai; Shintaro Nakano; Ritsushi Kato; Joachim A. Behar

doi:10.1038/s41597-025-04777-4

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

Kenta Tsutsui, Shany Biton Brimer, Noam Ben-Moshe, Jean Marc Sellal, Julien Oster, Hitoshi Mori, Yoshifumi Ikeda, Takahide Arai, Shintaro Nakano, Ritsushi Kato, Joachim A. Behar

Biomedical Engineering

Technion - Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

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/.

Original language	English
Article number	454
Journal	Scientific data
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41597-025-04777-4
State	Published - Dec 2025

All Science Journal Classification (ASJC) codes

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

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10.1038/s41597-025-04777-4

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title = "SHDB-AF: a Japanese Holter ECG database of atrial fibrillation",

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/.",

author = "Kenta Tsutsui and Brimer, {Shany Biton} and Noam Ben-Moshe and Sellal, {Jean Marc} and Julien Oster and Hitoshi Mori and Yoshifumi Ikeda and Takahide Arai and Shintaro Nakano and Ritsushi Kato and Behar, {Joachim A.}",

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month = dec,

doi = "10.1038/s41597-025-04777-4",

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AU - Tsutsui, Kenta

AU - Brimer, Shany Biton

AU - Ben-Moshe, Noam

AU - Sellal, Jean Marc

AU - Oster, Julien

AU - Mori, Hitoshi

AU - Ikeda, Yoshifumi

AU - Arai, Takahide

AU - Nakano, Shintaro

AU - Kato, Ritsushi

AU - Behar, Joachim A.

PY - 2025/12

Y1 - 2025/12

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AB - 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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ER -

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

Abstract

All Science Journal Classification (ASJC) codes

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