Lee Soojeong, Joshi Gyanendra Prasad, Son Chang-Hwan, Lee Gangseong
Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea.
Department of Software Science & Engineering, Kunsan National University, 558 Daehak-ro, Gunsan-si 54150, Republic of Korea.
Diagnostics (Basel). 2023 Feb 15;13(4):736. doi: 10.3390/diagnostics13040736.
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.
无创血压估计对于心血管疾病和高血压患者至关重要。基于无袖带的血压估计最近因连续血压监测而备受关注。本文提出了一种在无袖带血压估计中结合高斯过程与混合最优特征决策（HOFD）的新方法。首先，基于所提出的混合最优特征决策，我们可以选择一种特征选择方法：稳健邻域成分分析（RNCA）、最小冗余最大相关（MRMR）和F检验。之后，基于滤波器的RNCA算法使用训练数据集通过最小化损失函数来获得加权函数。接下来，我们将高斯过程（GP）算法作为评估标准，用于确定最佳特征子集。因此，将GP与HOFD相结合可实现有效的特征选择过程。所提出的将高斯过程与RNCA算法相结合的方法表明，收缩压（SBP）的均方根误差（RMSE）为10.75 mmHg，舒张压（DBP）的均方根误差为8.02 mmHg，低于传统算法。实验结果表明所提出的算法非常有效。
