论文标题
迈向M-Health的辅助诊断：一种用于大脑自动调节指数的灰色盒神经模型
Towards Assistive Diagnoses in m-Health: A Gray-box Neural Model for Cerebral Autoregulation Index
论文作者
论文摘要
脑自动调节系统（CAS）是一种机制，旨在调节大脑循环系统发生的压力变化。目前，仅存在侵入性方法，而它们又不用于防止脑血管事故。如今，M-Health的新兴概念允许使用移动设备来协助大脑自动调节指数（ARI）。为此，有必要找到新型模型，以使用血压值近似ARI。这项工作提出了一个灰盒神经模型，以找到动脉血压（ABP）与脑血流速度（CBFV）之间的关系，以获得ARI。与Aaslid-Tiecks模型相比，使用现象学模型使用现象学模型显示出良好的性能。
The cerebral autoregulation system (CAS), is a mechanism which aims to regulate pressure variations occurring in the cerebral circulatory system. At present, there only exist invasive methods and, in turn, they are not used to prevent cerebrovascular accidents. Nowadays, the emergent concept of m-Health allows to use mobile devices to assist the cerebral autoregulation index (ARI). For this, it is necessary to find novel models which allow to approximate the ARI by using the blood pressure value. This work proposes a gray-box neural model to find a relation between the arterial blood pressure (ABP) and the cerebral blood flow velocity (CBFV) in order to obtain the ARI. Preliminary results show good performance by using a phenomenological model in comparison to the Aaslid-Tiecks model.
