QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning
<p dir="ltr">Patients with hyperglycemia require routine glucose monitoring to effectively treat their condition. We have developed a lightweight wristband device to capture Photoplethysmography (PPG) signals. We collected PPG signals, demographic information, and blood pressure data...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513542142558208 |
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| author | Md Nazmul Islam Shuzan (21842426) |
| author2 | Moajjem Hossain Chowdhury (21842429) Muhammad E. H. Chowdhury (14150526) Khalid Abualsaud (16888701) Elias Yaacoub (14150586) Md Ahasan Atick Faisal (21842432) Mazun Alshahwani (21842435) Noora Al Bordeni (21842438) Fatima Al-Kaabi (21842441) Sara Al-Mohannadi (21842444) Sakib Mahmud (15302404) Nizar Zorba (16888728) |
| author2_role | author author author author author author author author author author author |
| author_facet | Md Nazmul Islam Shuzan (21842426) Moajjem Hossain Chowdhury (21842429) Muhammad E. H. Chowdhury (14150526) Khalid Abualsaud (16888701) Elias Yaacoub (14150586) Md Ahasan Atick Faisal (21842432) Mazun Alshahwani (21842435) Noora Al Bordeni (21842438) Fatima Al-Kaabi (21842441) Sara Al-Mohannadi (21842444) Sakib Mahmud (15302404) Nizar Zorba (16888728) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md Nazmul Islam Shuzan (21842426) Moajjem Hossain Chowdhury (21842429) Muhammad E. H. Chowdhury (14150526) Khalid Abualsaud (16888701) Elias Yaacoub (14150586) Md Ahasan Atick Faisal (21842432) Mazun Alshahwani (21842435) Noora Al Bordeni (21842438) Fatima Al-Kaabi (21842441) Sara Al-Mohannadi (21842444) Sakib Mahmud (15302404) Nizar Zorba (16888728) |
| dc.date.none.fl_str_mv | 2024-06-07T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3404971 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/QU-GM_An_IoT_Based_Glucose_Monitoring_System_From_Photoplethysmography_Blood_Pressure_and_Demographic_Data_Using_Machine_Learning/29715887 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Continuous glucose monitoring (CGM) Internet of Things (IoT) machine learning photoplethysmography (PPG) wearable device Glucose Diabetes Blood Biomedical monitoring Monitoring Skin Insulin Internet of Things Machine learning Photoplethysmography |
| dc.title.none.fl_str_mv | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Patients with hyperglycemia require routine glucose monitoring to effectively treat their condition. We have developed a lightweight wristband device to capture Photoplethysmography (PPG) signals. We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. Blood glucose was estimated, and diabetic severity (normal, warning, and dangerous) was stratified using Mel frequency cepstral coefficients, time, frequency, and statistical features from PPG and their derivative signals along with physiological parameters. Bagged Ensemble Trees outperform other algorithms in estimating blood glucose level with a correlation coefficient of 0.90. The proposed model’s prediction was all in Zone A and B in the Clarke Error Grid analysis. The predictions are thus clinically acceptable. Furthermore, K-nearest neighbor model classified the severity levels with an accuracy of 98.12%. Furthermore, the proposed models were deployed in Amazon Web Server. The wristband is connected to an Android mobile application to collect real-time data and update the estimated glucose and diabetic severity every 10-seconds, which will allow the users to gain better control of their diabetic health.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2024.3404971" target="_blank">https://dx.doi.org/10.1109/access.2024.3404971</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ef28200c8153b4b7be2ed966ca002b57 |
| identifier_str_mv | 10.1109/access.2024.3404971 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29715887 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine LearningMd Nazmul Islam Shuzan (21842426)Moajjem Hossain Chowdhury (21842429)Muhammad E. H. Chowdhury (14150526)Khalid Abualsaud (16888701)Elias Yaacoub (14150586)Md Ahasan Atick Faisal (21842432)Mazun Alshahwani (21842435)Noora Al Bordeni (21842438)Fatima Al-Kaabi (21842441)Sara Al-Mohannadi (21842444)Sakib Mahmud (15302404)Nizar Zorba (16888728)EngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningContinuous glucose monitoring (CGM)Internet of Things (IoT)machine learningphotoplethysmography (PPG)wearable deviceGlucoseDiabetesBloodBiomedical monitoringMonitoringSkinInsulinInternet of ThingsMachine learningPhotoplethysmography<p dir="ltr">Patients with hyperglycemia require routine glucose monitoring to effectively treat their condition. We have developed a lightweight wristband device to capture Photoplethysmography (PPG) signals. We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. Blood glucose was estimated, and diabetic severity (normal, warning, and dangerous) was stratified using Mel frequency cepstral coefficients, time, frequency, and statistical features from PPG and their derivative signals along with physiological parameters. Bagged Ensemble Trees outperform other algorithms in estimating blood glucose level with a correlation coefficient of 0.90. The proposed model’s prediction was all in Zone A and B in the Clarke Error Grid analysis. The predictions are thus clinically acceptable. Furthermore, K-nearest neighbor model classified the severity levels with an accuracy of 98.12%. Furthermore, the proposed models were deployed in Amazon Web Server. The wristband is connected to an Android mobile application to collect real-time data and update the estimated glucose and diabetic severity every 10-seconds, which will allow the users to gain better control of their diabetic health.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2024.3404971" target="_blank">https://dx.doi.org/10.1109/access.2024.3404971</a></p>2024-06-07T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3404971https://figshare.com/articles/journal_contribution/QU-GM_An_IoT_Based_Glucose_Monitoring_System_From_Photoplethysmography_Blood_Pressure_and_Demographic_Data_Using_Machine_Learning/29715887CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297158872024-06-07T06:00:00Z |
| spellingShingle | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning Md Nazmul Islam Shuzan (21842426) Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Continuous glucose monitoring (CGM) Internet of Things (IoT) machine learning photoplethysmography (PPG) wearable device Glucose Diabetes Blood Biomedical monitoring Monitoring Skin Insulin Internet of Things Machine learning Photoplethysmography |
| status_str | publishedVersion |
| title | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning |
| title_full | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning |
| title_fullStr | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning |
| title_full_unstemmed | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning |
| title_short | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning |
| title_sort | QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning |
| topic | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Continuous glucose monitoring (CGM) Internet of Things (IoT) machine learning photoplethysmography (PPG) wearable device Glucose Diabetes Blood Biomedical monitoring Monitoring Skin Insulin Internet of Things Machine learning Photoplethysmography |