Key result
Novel CGHOFD algorithm outperforms conventional models with ~11 mmHg systolic error in blood pressure estimation.
Why the study?
Noninvasive and continuous cuffless blood pressure estimation is crucial for cardiovascular and hypertension patients.
A novel machine learning algorithm combining Gaussian processes with hybrid optimal feature decision offers improved accuracy for continuous, noninvasive cuffless blood pressure estimation using PPG and ECG signals.
May support cuffless BP monitoring development; leaves open prospective validation before clinical use.
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.
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Lee et al. (2023) studied Blood pressure estimation (n=1,723). Gaussian process with hybrid optimal feature decision (CGHOFD) algorithm vs. Conventional machine learning algorithms (SVM, ANN, GP) was evaluated on Root mean square error (RMSE) for SBP and DBP. The proposed CGHOFD algorithm achieved root mean square errors of 10.75 mmHg for systolic and 8.02 mmHg for diastolic blood pressure, improving upon conventional algorithms.
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