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

IEEE Access

Muhammad E. H. Chowdhury¹  Amith Khandakar¹  Moajjem Hossain Chowdhury¹  Md. Nazmul Islam Shuzan²  Md. Shafayet Hossain³  Mamun Bin Ibne Reaz³  Sawal Hamid Md. Ali³  Mohammad Monir Uddin⁴  Zaid Bin Mahbub⁴ 

[1] Department of Electrical Engineering, Qatar University, Doha, Qatar;Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh;Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Selangor, Bangi, Malaysia;Department of Mathematics and Physics, North South University, Dhaka, Bangladesh;

关键词: Photoplethysmogram;    respiration rate;    machine learning;    feature selection;    motion artifact correction;    Gaussian process regression;   

DOI  :  10.1109/ACCESS.2021.3095380

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown