Key result
ECG complexity machine learning model estimates systolic BP within ~8 mmHg error after calibration.
Why the study?
Can a machine learning method using only ECG signals accurately estimate systolic, diastolic, and mean arterial blood pressure?
Observational (n=51)
Yes
Can a machine learning method using only ECG signals accurately estimate systolic, diastolic, and mean arterial blood pressure?
Effect estimate: MAE 7.72 mmHg
A machine learning approach using ECG signal complexity analysis can estimate blood pressure with mean absolute errors close to certified medical devices when calibrated.
ECG-based ML BP estimates warrant further development; leaves open clinical adoption pending larger prospective validation.
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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Simjanoska et al. (2018) conducted an observational in Blood pressure estimation (n=51). Machine learning-based blood pressure estimation from ECG signals vs. Reference blood pressure measurements (cuff-based or continuous arterial monitor) was evaluated on Mean absolute error (MAE) for systolic blood pressure (SBP) prediction after calibration (MAE 7.72 mmHg). A machine learning method using complexity analysis of ECG signals achieved a mean absolute error of 7.72 mmHg for systolic blood pressure and 9.45 mmHg for diastolic blood pressure after calibration.
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