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

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques.
Author Information (click to view)

Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A,


Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A, (click to view)

Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A,

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Sensors (Basel, Switzerland) 2018 04 1118(4) pii E1160
Abstract
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.

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