Why the study?
Does a random forest machine-learning method using PPG and ECG signals improve beat-to-beat ambulatory blood pressure estimation compared to the pulse transit time method?
Does a random forest machine-learning method using PPG and ECG signals improve beat-to-beat ambulatory blood pressure estimation compared to the pulse transit time method?
A random forest machine-learning approach using PPG and ECG signals outperforms the standard pulse transit time method for continuous, cuff-less beat-to-beat blood pressure estimation.
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May enhance cuffless BP monitoring accuracy; leaves open prospective clinical validation before adoption.
He et al. (2016) studied this question.
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