Summary of Shdb-af: a Japanese Holter Ecg Database Of Atrial Fibrillation, by Kenta Tsutsui et al.
SHDB-AF: a Japanese Holter ECG database of atrial fibrillation
by 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
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Medical Physics (physics.med-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach for diagnosing atrial fibrillation (AF) has been developed, leveraging recent advancements in deep learning (DL). The proposed DL model aims to enhance diagnostic accuracy by being robust and generalizable across various patient demographics. A novel open-sourced Holter ECG database, the Saitama Heart Database Atrial Fibrillation (SHDB-AF), is presented, featuring data from 100 unique Japanese patients with paroxysmal AF. Each record in SHDB-AF is 24 hours long and contains 24 million seconds of electrocardiogram (ECG) data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Atrial fibrillation affects many people’s quality of life and can cause serious complications. New computer learning techniques are helping doctors diagnose this condition more accurately. A team created a special database with recordings from Japanese patients who have this condition. The database is open for others to use, which can help improve diagnosis methods. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
