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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2021
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| _version_ | 1864513560261951488 |
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| author | Md. Nazmul Islam Shuzan (16888827) |
| author2 | Moajjem Hossain Chowdhury (16888830) Md. Shafayet Hossain (16888833) Muhammad E. H. Chowdhury (14150526) Mamun Bin Ibne Reaz (16875933) Mohammad Monir Uddin (16888836) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Sawal Hamid Md. Ali (16888839) |
| author2_role | author author author author author author author author |
| author_facet | Md. Nazmul Islam Shuzan (16888827) Moajjem Hossain Chowdhury (16888830) Md. Shafayet Hossain (16888833) Muhammad E. H. Chowdhury (14150526) Mamun Bin Ibne Reaz (16875933) Mohammad Monir Uddin (16888836) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Sawal Hamid Md. Ali (16888839) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Nazmul Islam Shuzan (16888827) Moajjem Hossain Chowdhury (16888830) Md. Shafayet Hossain (16888833) Muhammad E. H. Chowdhury (14150526) Mamun Bin Ibne Reaz (16875933) Mohammad Monir Uddin (16888836) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Sawal Hamid Md. Ali (16888839) |
| dc.date.none.fl_str_mv | 2021-07-07T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3095380 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Non-Invasive_Estimation_of_Respiration_Rate_From_Motion_Corrupted_Photoplethysmograph_Signal_Using_Machine_Learning_Model/24038994 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 Machine learning Feature selection Motion artifact correction Gaussian process regression |
| dc.title.none.fl_str_mv | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <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. 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3095380" target="_blank">https://dx.doi.org/10.1109/access.2021.3095380</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1a6e4cee0b73e2cfa9a3d49c3e280ea1 |
| identifier_str_mv | 10.1109/access.2021.3095380 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24038994 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning ModelMd. Nazmul Islam Shuzan (16888827)Moajjem Hossain Chowdhury (16888830)Md. Shafayet Hossain (16888833)Muhammad E. H. Chowdhury (14150526)Mamun Bin Ibne Reaz (16875933)Mohammad Monir Uddin (16888836)Amith Khandakar (14151981)Zaid Bin Mahbub (16869975)Sawal Hamid Md. Ali (16888839)EngineeringBiomedical engineeringInformation and computing sciencesData management and data scienceMachine learningFeature extractionEstimationElectrocardiographyMotion artifactsMotion segmentationMonitoringMachine learning algorithmsPhotoplethysmogramRespiration rateMachine learningFeature selectionMotion artifact correctionGaussian process regression<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. 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3095380" target="_blank">https://dx.doi.org/10.1109/access.2021.3095380</a></p>2021-07-07T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3095380https://figshare.com/articles/journal_contribution/A_Novel_Non-Invasive_Estimation_of_Respiration_Rate_From_Motion_Corrupted_Photoplethysmograph_Signal_Using_Machine_Learning_Model/24038994CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240389942021-07-07T00:00:00Z |
| spellingShingle | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model Md. Nazmul Islam Shuzan (16888827) 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 Machine learning Feature selection Motion artifact correction Gaussian process regression |
| status_str | publishedVersion |
| 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 | A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model |
| topic | 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 Machine learning Feature selection Motion artifact correction Gaussian process regression |