Key result
A deep belief network-deep neural network ensemble estimator provided lower standard deviation of error, mean error, and mean absolute error for blood pressure estimation than conventional methods.
A novel DBN-DNN ensemble estimator improves the accuracy of blood pressure estimation from oscillometry signals compared to conventional methods.
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May support cuffless BP monitoring development; hypothesis-generating and requires prospective clinical validation before practice change.
Lee et al. (2017) studied Blood pressure estimation. Deep belief network-deep neural network (DBN-DNN) ensemble estimator vs. Conventional methods was evaluated on Systolic and diastolic blood pressure estimation error (standard deviation of error, mean error, and mean absolute error). A deep belief network-deep neural network ensemble estimator provided lower standard deviation of error, mean error, and mean absolute error for blood pressure estimation than conventional methods.
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