Why the study?
Currently used blood pressure measurement devices face limitations in accuracy, continuity, and comfort.
Does a random forest regression model accurately estimate blood pressure from ECG signals alone?
Does a random forest regression model accurately estimate blood pressure from ECG signals alone?
Blood pressure can be estimated from ECG signals alone with reasonable accuracy using random forest regression, potentially offering a continuous and comfortable measurement alternative.
ECG-only BP estimation warrants clinical caution; leaves open prospective validation before any adoption.
To overcome limitations of currently used blood pressure measurement devices in accuracy, continuity and comfort, we propose an approach for blood pressure estimation from electrocardiogram (ECG) signals only. Thereby, statistical signal features are extracted from the ECG which, eventually, serve as input to a random forest regression. The method is trained and tested on MIMIC III waveform data with a large range of blood pressure values. It obtains a mean absolute error ± standard deviation of 3.73 ± 5.19 mmHg for diastolic blood pressure (DBP) and 5.92 ± 7.23 mmHg for systolic blood pressure (SBP), with Pearson coefficients Υ DBP =0.92 and Υ SBP =0.91 respectively.
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Wuerich et al. (2022) studied this question.
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