A machine learning method for blood pressure estimation using only ECG signals achieved a mean absolute error of 7.72 mmHg for SBP, 9.45 mmHg for DBP, and 8.13 mmHg for MAP after calibration.
Does a machine learning method using ECG signals accurately estimate systolic, diastolic, and mean arterial blood pressure?
A machine learning approach using only ECG signals with probability distribution-based calibration can estimate blood pressure with accuracy approaching certified medical devices.
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.
Simjanoska et al. (Wed,) conducted a other in Blood pressure estimation (n=51). Machine learning method for BP estimation from ECG was evaluated on Mean absolute error (MAE) for SBP, DBP, and MAP prediction. A machine learning method for blood pressure estimation using only ECG signals achieved a mean absolute error of 7.72 mmHg for SBP, 9.45 mmHg for DBP, and 8.13 mmHg for MAP after calibration.