Exploring New Frontiers in Atrial Fibrillation Diagnosis with AI and ECG Data 🧠💓
We are glad to annonce our latest publication in Scientific Data entitled "SHDB-AF: a Japanese Holter ECG database of atrial fibrillation" which was led by Dr. (MD) Kenta Tsutsui and AIMLab. doctoral student Shany Biton.
Atrial fibrillation (AF) is a prevalent atrial arrhythmia that significantly impacts quality of life and increases the risk of complications such as embolic stroke and heart failure. Recent breakthroughs in machine learning (ML) and deep learning (DL) are paving the way for more accurate and efficient AF diagnoses.
However, for DL models to truly enhance diagnostic performance, they must be robust across various patient factors, such as ethnicity, age, and sex. While several ECG databases are available for research, there remains a gap in representation from certain populations.
We are glad to introduce the Saitama Heart Database Atrial Fibrillation (SHDB-AF) — a new open-source Holter ECG dataset from Japan. This new resource offers:
* 128 ECG records from 122 unique patients.
* 24-hour long, two-channel ECGs totaling 21.6 million seconds of data.
* Detailed clinical information.
The SHDB-AF dataset is now available for research and can be accessed at: https://lnkd.in/drXZMt-b
Publication: https://lnkd.in/d8zBmZyS
This is an exciting opportunity for the scientific community to further improve AF detection using diverse, high-quality data. 🔍💡
hashtag#AtrialFibrillation hashtag#DeepLearning hashtag#MachineLearning hashtag#ECG hashtag#MedicalResearch hashtag#AIinHealthcare hashtag#HealthTech hashtag#Physionet hashtag#DataScience hashtag#Cardiology hashtag#AFDetection
Technion - Israel Institute of Technology
Faculty of Biomedical Engineering, Technion
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