Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques
Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Meth...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/4/1160 |
| _version_ | 1798004958548721664 |
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| author | Monika Simjanoska Martin Gjoreski Matjaž Gams Ana Madevska Bogdanova |
| author_facet | Monika Simjanoska Martin Gjoreski Matjaž Gams Ana Madevska Bogdanova |
| author_sort | Monika Simjanoska |
| collection | DOAJ |
| description | Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation. |
| first_indexed | 2024-04-11T12:32:50Z |
| format | Article |
| id | doaj.art-e8870b0c0b4f4b1c8bd11db3ad25f60b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:32:50Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e8870b0c0b4f4b1c8bd11db3ad25f60b2022-12-22T04:23:43ZengMDPI AGSensors1424-82202018-04-01184116010.3390/s18041160s18041160Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning TechniquesMonika Simjanoska0Martin Gjoreski1Matjaž Gams2Ana Madevska Bogdanova3Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovikj 16, 1000 Skopje, MacedoniaDepartment of Intelligent Systems, Jožef Stefan Institute, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, SloveniaDepartment of Intelligent Systems, Jožef Stefan Institute, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, SloveniaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovikj 16, 1000 Skopje, MacedoniaBackground: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.http://www.mdpi.com/1424-8220/18/4/1160blood pressureECGmachine learningcomplexity analysisclassificationregressionstacking |
| spellingShingle | Monika Simjanoska Martin Gjoreski Matjaž Gams Ana Madevska Bogdanova Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques Sensors blood pressure ECG machine learning complexity analysis classification regression stacking |
| title | Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques |
| title_full | Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques |
| title_fullStr | Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques |
| title_full_unstemmed | Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques |
| title_short | Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques |
| title_sort | non invasive blood pressure estimation from ecg using machine learning techniques |
| topic | blood pressure ECG machine learning complexity analysis classification regression stacking |
| url | http://www.mdpi.com/1424-8220/18/4/1160 |
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