Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults
Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults
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DOI：
10.1109/tbme.2015.2389149
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发表时间：
2015-05-01
影响因子：
4.6
通讯作者：
Mihailidis, Alex
中科院分区：
文献类型：
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作者：
Akl, Ahmad;Taati, Babak;Mihailidis, Alex
目前的痴呆症诊断过程导致很高比例的病例延迟检测。为了解决这个问题，在本文中，我们探讨了自主检测轻度认知障碍（MCI）的老年人群体的可行性。我们实现了一种配备机器学习范式的信号处理方法，以处理和分析使用基于家庭的非侵入式传感技术获取的真实数据。使用传感器和97名受试者的临床数据（平均三年时间），计算了与受试者步行速度和家中一般活动相关的许多指标。这些措施的不同时间跨度被用来生成特征向量，以训练和测试两种机器学习算法，即支持向量机和随机森林。我们能够在24周的时间窗内自主检测老年人MCI，ROC曲线下面积为0.97，精确-回忆曲线下面积为0.93。这项研究具有重要意义，因为它可能有助于早期发现老年人的认知障碍。
The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing approach equipped with a machine learning paradigm to process and analyze real-world data acquired using home-based unobtrusive sensing technologies. Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of three years, a number of measures associated with the subjects' walking speed and general activity in the home were calculated. Different time spans of these measures were used to generate feature vectors to train and test two machine learning algorithms namely support vector machines and random forests. We were able to autonomously detect MCI in older adults with an area under the ROC curve of 0.97 and an area under the precision-recall curve of 0.93 using a time window of 24 weeks. This study is of great significance since it can potentially assist in the early detection of cognitive impairment in older adults.