Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks
Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attach...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/7/2303 |
| _version_ | 1827696818316640256 |
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| author | Woojoon Seok Kwang Jin Lee Dongrae Cho Jongryun Roh Sayup Kim |
| author_facet | Woojoon Seok Kwang Jin Lee Dongrae Cho Jongryun Roh Sayup Kim |
| author_sort | Woojoon Seok |
| collection | DOAJ |
| description | Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7. |
| first_indexed | 2024-03-10T12:54:43Z |
| format | Article |
| id | doaj.art-f900e16cafe44770bc9dd50f115e0749 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:54:43Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f900e16cafe44770bc9dd50f115e07492023-11-21T12:02:04ZengMDPI AGSensors1424-82202021-03-01217230310.3390/s21072303Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural NetworksWoojoon Seok0Kwang Jin Lee1Dongrae Cho2Jongryun Roh3Sayup Kim4Human Convergence Technology R&D Department, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 15588, KoreaDeep Medi Research Institute of Technology, Deep Medi Inc., Seoul 06232, KoreaDeep Medi Research Institute of Technology, Deep Medi Inc., Seoul 06232, KoreaHuman Convergence Technology R&D Department, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 15588, KoreaHuman Convergence Technology R&D Department, Korea Institute of Industrial Technology, 143 Hanggaulro, Ansan 15588, KoreaHypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.https://www.mdpi.com/1424-8220/21/7/2303cuffless blood pressure monitoring systemhypertensionballistocardiogram (BCG)convolutional neural network (CNN) |
| spellingShingle | Woojoon Seok Kwang Jin Lee Dongrae Cho Jongryun Roh Sayup Kim Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks Sensors cuffless blood pressure monitoring system hypertension ballistocardiogram (BCG) convolutional neural network (CNN) |
| title | Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks |
| title_full | Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks |
| title_fullStr | Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks |
| title_full_unstemmed | Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks |
| title_short | Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks |
| title_sort | blood pressure monitoring system using a two channel ballistocardiogram and convolutional neural networks |
| topic | cuffless blood pressure monitoring system hypertension ballistocardiogram (BCG) convolutional neural network (CNN) |
| url | https://www.mdpi.com/1424-8220/21/7/2303 |
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