Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China.
Biomed Eng Online. 2019 Dec 27;18(1):124. doi: 10.1186/s12938-019-0740-4.
Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hypertension stage 1 and hypertension stage 2. The quantitative analysis of hypertension helps determine disease status, prognosis assessment, guidance and management, but is not well studied in the framework of machine learning.
We proposed empirical kernel mapping-based kernel extreme learning machine plus (EKM-KELM+) classifier to discriminate different blood pressure grades in adults from structural brain MR images. ELM+ is the extended version of ELM, which integrates the additional privileged information about training samples in ELM to help train a more effective classifier. In this work, we extracted gray matter volume (GMV), white matter volume, cerebrospinal fluid volume, cortical surface area, cortical thickness from structural brain MR images, and constructed brain network features based on thickness. After feature selection and EKM, the enhanced features are obtained. Then, we select one feature type as the main feature to feed into KELM+, and the rest of the feature types are PI to assist the main feature to train 5 KELM+ classifiers. Finally, the 5 KELM+ classifiers are ensemble to predict classification result in the test stage, while PI is not used during testing.
We evaluated the performance of the proposed EKM-KELM+ method using four grades of hypertension data (73 samples for each grade). The experimental results show that the GMV performs observably better than any other feature types with a comparatively higher classification accuracy of 77.37% (Grade 1 vs. Grade 2), 93.19% (Grade 1 vs. Grade 3), and 95.15% (Grade 1 vs. Grade 4). The most discriminative brain regions found using our method are olfactory, orbitofrontal cortex (inferior), supplementary motor area, etc. CONCLUSIONS: Using region of interest features and brain network features, EKM-KELM+ is proposed to study the most discriminative regions that have obvious structural changes in different blood pressure grades. The discriminative features that are selected using our method are consistent with the existing neuroimaging studies. Moreover, our study provides a potential approach to take effective interventions in the early period, when the blood pressure makes minor impacts on the brain structure and function.
高血压会增加心血管疾病和认知障碍的风险。血压有四个类别：正常、升高、高血压 1 期和高血压 2 期。高血压的定量分析有助于确定疾病状态、预后评估、指导和管理，但在机器学习框架中研究得还不够。
我们提出了基于经验核映射的核极限学习机加（EKM-KELM+）分类器，用于从成人的结构脑磁共振图像中区分不同的血压等级。ELM+是 ELM 的扩展版本，它将关于训练样本的额外特权信息集成到 ELM 中，以帮助训练更有效的分类器。在这项工作中，我们从结构脑磁共振图像中提取灰质体积（GMV）、白质体积、脑脊液体积、皮质表面积和皮质厚度，并构建基于厚度的脑网络特征。在特征选择和 EKM 之后，得到增强的特征。然后，我们选择一种特征类型作为主特征输入到 KELM+中，其余的特征类型作为 PI 来辅助主特征训练 5 个 KELM+分类器。最后，在测试阶段，这 5 个 KELM+分类器被集成以预测分类结果，而在测试过程中不使用 PI。
我们使用四级高血压数据（每个等级 73 个样本）来评估所提出的 EKM-KELM+方法的性能。实验结果表明，GMV 的表现明显优于任何其他特征类型，分类准确率分别为 77.37%（1 级与 2 级）、93.19%（1 级与 3 级）和 95.15%（1 级与 4 级）。我们的方法发现的最具区分性的脑区是嗅觉、眶额皮质（下）、运动辅助区等。
使用感兴趣区特征和脑网络特征，我们提出了 EKM-KELM+来研究在不同血压等级下具有明显结构变化的最具区分性的区域。我们的方法选择的判别特征与现有的神经影像学研究一致。此外，我们的研究提供了一种潜在的方法，可以在血压对大脑结构和功能影响较小的早期阶段采取有效的干预措施。
