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Feature Selection of Photoplethysmograph Data in Machine Learning. Abstract: Photoplethysmography signals are more responsive to changes in blood volume, not ...
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This study proposes a new approach to optimize the statistical parameters of regression produced by PPG signals. The fingertip pulse wave device samples the PPG ...
Abstract— Photoplethysmography signals are more responsive to changes in blood volume, not vascular pressure. Nowadays, more and more research is being.
So in this paper, a new hybrid approach of machine learning combining feature selection and classification algorithms is presented. The model is examined with ...
Aug 10, 2022 · Supervised ML methods are comprised of three crucial steps- feature extraction and selection, classifier training, and lastly evaluation ( ...
Bibliographic details on Feature Selection of Photoplethysmograph Data in Machine Learning.
Traditional machine learning methods for automatic analysis of PPG data typically involve manual feature extraction [5, 6] . Researchers need to firstly ...
Suzuki and Ryu [34] proposed a PPG feature selection method for estimating SBP. The data were acquired using a cuff-based BP device attached to the left upper ...
Aug 17, 2023 · (1) We provide a thorough comparison of different machine learning techniques for estimating HbA1c using wrist PPG data. Here, we evaluate the ...
This work proposes to use the wavelet scattering transform as a feature extraction technique to obtain features from PPG data and combine it with clinical data ...