A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model
Author | Shuzan, Md. Nazmul Islam |
Author | Chowdhury, Moajjem Hossain |
Author | Hossain, Md. Shafayet |
Author | Chowdhury, Muhammad E. H. |
Author | Reaz, Mamun Bin Ibne |
Author | Uddin, Mohammad Monir |
Author | Khandakar, Amith |
Author | Mahbub, Zaid Bin |
Author | Ali, Sawal Hamid Md. |
Available date | 2023-04-17T06:57:46Z |
Publication Date | 2021 |
Publication Name | IEEE Access |
Resource | Scopus |
Abstract | 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 real-Time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-Tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal. 2013 IEEE. |
Sponsor | Corresponding authors: Muhammad E. H. Chowdhury (mchowdhury@qu.edu.qa), Mamun Bin Ibne Reaz (mamun@ukm.edu.my), and Md. Shafayet Hossain (p108100@siswa.ukm.edu.my) This work was supported in part by the Qatar National Research under Grant NPRP12S-0227-190164, and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | feature selection Gaussian process regression machine learning motion artifact correction Photoplethysmogram respiration rate |
Type | Article |
Pagination | 96775-96790 |
Volume Number | 9 |
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Electrical Engineering [2611 items ]