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Sensors 2018, 18(4), 1160;

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovikj 16, 1000 Skopje, Macedonia
Department of Intelligent Systems, Jožef Stefan Institute, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 24 January 2018 / Revised: 13 March 2018 / Accepted: 26 March 2018 / Published: 11 April 2018
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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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. View Full-Text
Keywords: blood pressure; ECG; machine learning; complexity analysis; classification; regression; stacking blood pressure; ECG; machine learning; complexity analysis; classification; regression; stacking

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Simjanoska, M.; Gjoreski, M.; Gams, M.; Madevska Bogdanova, A. Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques. Sensors 2018, 18, 1160.

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