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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.16974 |
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| _version_ | 1866909230560182272 |
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| author | Tsutsui, Kenta Brimer, Shany Biton Ben-Moshe, Noam Sellal, Jean Marc Oster, Julien Mori, Hitoshi Ikeda, Yoshifumi Arai, Takahide Nakano, Shintaro Kato, Ritsushi Behar, Joachim A. |
| author_facet | Tsutsui, Kenta Brimer, Shany Biton Ben-Moshe, Noam Sellal, Jean Marc Oster, Julien Mori, Hitoshi Ikeda, Yoshifumi Arai, Takahide Nakano, Shintaro Kato, Ritsushi Behar, Joachim A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16974 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SHDB-AF: a Japanese Holter ECG database of atrial fibrillation Tsutsui, Kenta Brimer, Shany Biton Ben-Moshe, Noam Sellal, Jean Marc Oster, Julien Mori, Hitoshi Ikeda, Yoshifumi Arai, Takahide Nakano, Shintaro Kato, Ritsushi Behar, Joachim A. Machine Learning Medical Physics 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. |
| title | SHDB-AF: a Japanese Holter ECG database of atrial fibrillation |
| topic | Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2406.16974 |