A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms
Background and objective: Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomforta...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024038106 |
| _version_ | 1797224218046234624 |
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| author | Araf Nishan S. M. Taslim Uddin Raju Md Imran Hossain Safin Ahmed Dipto S. M. Tanvir Uddin Asif Sijan Md Abu Shahid Chowdhury Ashfaq Ahmad Md Mahamudul Hasan Khan |
| author_facet | Araf Nishan S. M. Taslim Uddin Raju Md Imran Hossain Safin Ahmed Dipto S. M. Tanvir Uddin Asif Sijan Md Abu Shahid Chowdhury Ashfaq Ahmad Md Mahamudul Hasan Khan |
| author_sort | Araf Nishan |
| collection | DOAJ |
| description | Background and objective: Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. Methods: In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. Results: The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. Conclusion: The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features. |
| first_indexed | 2024-04-24T13:49:37Z |
| format | Article |
| id | doaj.art-b2b4bc53886b4c548d4ff5333720dbb5 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T13:49:37Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-b2b4bc53886b4c548d4ff5333720dbb52024-04-04T05:05:53ZengElsevierHeliyon2405-84402024-03-01106e27779A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithmsAraf Nishan0S. M. Taslim Uddin Raju1Md Imran Hossain2Safin Ahmed Dipto3S. M. Tanvir Uddin4Asif Sijan5Md Abu Shahid Chowdhury6Ashfaq Ahmad7Md Mahamudul Hasan Khan8Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, BangladeshDepartment of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh; Corresponding author.Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, BangladeshDepartment of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, BangladeshDepartment of Electrical and Electronic Engineering, Dhaka University of Engineering & Technology, Gazipur, BangladeshDepartment of Software Engineering, American International University, Dhaka, BangladeshDepartment of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna - 9203, BangladeshDepartment of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, BangladeshDepartment of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, BangladeshBackground and objective: Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. Methods: In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. Results: The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. Conclusion: The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.http://www.sciencedirect.com/science/article/pii/S2405844024038106Continuous blood pressure (BP)Photoplethysmogram (PPG)Feature extractionFeature selectionNonlinear regression models |
| spellingShingle | Araf Nishan S. M. Taslim Uddin Raju Md Imran Hossain Safin Ahmed Dipto S. M. Tanvir Uddin Asif Sijan Md Abu Shahid Chowdhury Ashfaq Ahmad Md Mahamudul Hasan Khan A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms Heliyon Continuous blood pressure (BP) Photoplethysmogram (PPG) Feature extraction Feature selection Nonlinear regression models |
| title | A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms |
| title_full | A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms |
| title_fullStr | A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms |
| title_full_unstemmed | A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms |
| title_short | A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms |
| title_sort | continuous cuffless blood pressure measurement from optimal ppg characteristic features using machine learning algorithms |
| topic | Continuous blood pressure (BP) Photoplethysmogram (PPG) Feature extraction Feature selection Nonlinear regression models |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024038106 |
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