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

<p>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. Howev...

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Bibliographic Details
Main Author: Md. Nazmul Islam Shuzan (16888827) (author)
Other Authors: Moajjem Hossain Chowdhury (16888830) (author), Md. Shafayet Hossain (16888833) (author), Muhammad E. H. Chowdhury (14150526) (author), Mamun Bin Ibne Reaz (16875933) (author), Mohammad Monir Uddin (16888836) (author), Amith Khandakar (14151981) (author), Zaid Bin Mahbub (16869975) (author), Sawal Hamid Md. Ali (16888839) (author)
Published: 2021
Subjects:
Engineering
Biomedical engineering
Information and computing sciences
Data management and data science
Machine learning
Feature extraction
Estimation
Electrocardiography
Motion artifacts
Motion segmentation
Monitoring
Machine learning algorithms
Photoplethysmogram
Respiration rate
Feature selection
Motion artifact correction
Gaussian process regression
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