A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a rea...

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Bibliographic Details
Main Authors: Md. Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, Md. Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Mohammad Monir Uddin, Amith Khandakar, Zaid Bin Mahbub, Sawal Hamid Md. Ali
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Photoplethysmogram
respiration rate
machine learning
feature selection
motion artifact correction
Gaussian process regression
Online Access:https://ieeexplore.ieee.org/document/9475991/

Internet

https://ieeexplore.ieee.org/document/9475991/

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