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Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms

Release time:2025-04-28 Hits:

Journal:IEEE journal of biomedical and health informatics

Translation or Not:no

Date of Publication:2024-02-01

First Author:Cheng Ding

Pre One:Sparse learned kernels for interpretable and efficient medical time series processing

Next One:Log-spectral matching gan: Ppg-based atrial fibrillation detection can be enhanced by gan-based data augmentation with integration of spectral loss

Cheng Ding

Gender:Male Education Level:With Certificate of Graduation for Doctorate Study Alma Mater:美国佐治亚理工大学 Degree:Doctoral Degree in Engineering School/Department:人工智能学院 E-Mail:

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Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms