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...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9475991/ |
| _version_ | 1818611899192836096 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Md. Nazmul Islam Shuzan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T15:37:39Z |
| format | Article |
| id | doaj.art-c14770de344a4f71a0cb31f1270ffd04 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T15:37:39Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c14770de344a4f71a0cb31f1270ffd042022-12-21T22:26:08ZengIEEEIEEE Access2169-35362021-01-019967759679010.1109/ACCESS.2021.30953809475991A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning ModelMd. Nazmul Islam Shuzan0https://orcid.org/0000-0002-3089-9737Moajjem Hossain Chowdhury1Md. Shafayet Hossain2https://orcid.org/0000-0001-5365-2182Muhammad E. H. Chowdhury3https://orcid.org/0000-0003-0744-8206Mamun Bin Ibne Reaz4https://orcid.org/0000-0002-0459-0365Mohammad Monir Uddin5Amith Khandakar6https://orcid.org/0000-0001-7068-9112Zaid Bin Mahbub7Sawal Hamid Md. Ali8https://orcid.org/0000-0002-4819-863XDepartment of Electrical and Computer Engineering, North South University, Dhaka, BangladeshDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Selangor, Bangi, MalaysiaDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Selangor, Bangi, MalaysiaDepartment of Mathematics and Physics, North South University, Dhaka, BangladeshDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Mathematics and Physics, North South University, Dhaka, BangladeshDepartment of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Selangor, Bangi, MalaysiaRespiratory 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.https://ieeexplore.ieee.org/document/9475991/Photoplethysmogramrespiration ratemachine learningfeature selectionmotion artifact correctionGaussian process regression |
| spellingShingle | 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 A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model IEEE Access Photoplethysmogram respiration rate machine learning feature selection motion artifact correction Gaussian process regression |
| title | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model |
| title_full | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model |
| title_fullStr | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model |
| title_full_unstemmed | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model |
| title_short | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model |
| title_sort | novel non invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model |
| topic | Photoplethysmogram respiration rate machine learning feature selection motion artifact correction Gaussian process regression |
| url | https://ieeexplore.ieee.org/document/9475991/ |
| work_keys_str_mv | AT mdnazmulislamshuzan anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT moajjemhossainchowdhury anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT mdshafayethossain anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT muhammadehchowdhury anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT mamunbinibnereaz anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT mohammadmoniruddin anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT amithkhandakar anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT zaidbinmahbub anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT sawalhamidmdali anovelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT mdnazmulislamshuzan novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT moajjemhossainchowdhury novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT mdshafayethossain novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT muhammadehchowdhury novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT mamunbinibnereaz novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT mohammadmoniruddin novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT amithkhandakar novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT zaidbinmahbub novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel AT sawalhamidmdali novelnoninvasiveestimationofrespirationratefrommotioncorruptedphotoplethysmographsignalusingmachinelearningmodel |