Information Engineering, Guangdong University of Technology, Guangzhou, China.
IET Syst Biol. 2021 Aug;15(6):184-191. doi: 10.1049/syb2.12033. Epub 2021 Sep 1.
Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes, namely the class of healthy blood pressure values and the class of prehypertension blood pressure values, as well as estimating the blood pressure values continuously only by employing photoplethysmograms. First, the denoising of photoplethysmograms is performed via a discrete cosine transform approach. Then, the features of the photoplethysmograms in both the time domain and the frequency domain are extracted. Next, the feature vectors are categorised into the two classes of blood pressure values by a multi-model fusion of the classifiers. Here, the support vector machine, the random forest and the K-nearest neighbour classifier are employed for performing the fusion. There are two types of blood pressure values. They are the systolic blood pressure values and the diastolic blood pressure values. For each class and each type of blood pressure values, support vector regression is used to estimate the blood pressure values. Since different classes and different types of blood pressure values are considered separately, the proposed method achieves an accurate estimation. The computed numerical simulation results show that the proposed method based on the multi-model fusion of the classifiers achieves both higher classification accuracy and higher regression accuracy than the individual classification methods.
高血压前期是联合国家委员会发布的第七份报告中定义的一种新的危险疾病。因此，及时发现高血压前期对于保护人类生命非常重要。本研究提出了一种方法，可将血压值分为两类，即健康血压值类和高血压前期血压值类，并仅通过使用光体积描记图连续估计血压值。首先，通过离散余弦变换方法对光体积描记图进行去噪。然后，提取光体积描记图在时域和频域中的特征。接下来，通过分类器的多模型融合将特征向量分类为两类血压值。在这里，支持向量机、随机森林和 K 最近邻分类器用于执行融合。有两种类型的血压值。它们是收缩压值和舒张压值。对于每种类别和每种类别血压值，使用支持向量回归来估计血压值。由于分别考虑了不同的类别和不同类型的血压值，因此所提出的方法实现了准确的估计。计算的数值模拟结果表明，基于分类器的多模型融合的所提出的方法比单个分类方法具有更高的分类精度和更高的回归精度。
