Combining the Gaussian process with the RNCA algorithm achieved lower root mean square errors for systolic (10.75 mmHg) and diastolic (8.02 mmHg) blood pressure compared to conventional algorithms.
Does combining a Gaussian process with hybrid optimal feature decision improve the accuracy of cuffless blood pressure estimation compared to conventional algorithms?
A novel machine learning approach combining Gaussian processes with hybrid optimal feature decision improves the accuracy of cuffless blood pressure estimation.
Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective.
Lee et al. (Wed,) conducted a other in Cardiovascular and hypertension. Gaussian process with hybrid optimal feature decision (HOFD) vs. Conventional algorithms was evaluated on Root mean square errors (RMSEs) for SBP and DBP. Combining the Gaussian process with the RNCA algorithm achieved lower root mean square errors for systolic (10.75 mmHg) and diastolic (8.02 mmHg) blood pressure compared to conventional algorithms.