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Deep Learning for PPG Quality Assessment by trung042005 is a document available to read on EtoBox.

What is Deep Learning for PPG Quality Assessment about?

This document presents a deep learning-based approach for assessing the quality of photoplethysmography (PPG) signals used in heart rate (HR) and heart rate variability (HRV) monitoring. The proposed method utilizes customized one-dimensional and two-dimensional convolutional neural networks (CNNs) to evaluate PPG signal reliability against electrocardiogram (ECG) data, achieving high accuracy rates. The study highlights the inadequacies of existing PPG quality assessment methods and provides an open-source

Author

trung042005

Language

EN