Department of Computer Science, University of Rostock, Rostock, Germany.
J Alzheimers Dis. 2014;38(1):121-32. doi: 10.3233/JAD-130272.
Early detection of behavioral changes in Alzheimer's disease (AD) would help the design and implementation of specific interventions.
The target of our investigation was to establish a correlation between diagnosis and unconstrained motion behavior in subjects without major clinical behavior impairments.
We studied everyday motion behavior in 23 dyads with one partner suffering from AD dementia and one cognitively healthy partner in the subjects' home, employing ankle-mounted three-axes accelerometric sensors. We determined frequency features obtained from the signal envelopes computed by an envelope detector for the carrier band 0.5 Hz to 5 Hz. Based on these features, we employed quadratic discriminant analysis for building models discriminating between AD patients and healthy controls.
After leave-one-out cross-validation, the classification accuracy of motion features reached 91% and was superior to the classification accuracy based on the Cohen-Mansfield Agitation Inventory (CMAI). Motion features were significantly correlated with MMSE and CMAI scores.
Our findings suggest that changes of everyday behavior are detectable in accelerometric behavior protocols even in the absence of major clinical behavioral impairments in AD.
阿尔茨海默病（AD）行为变化的早期检测有助于设计和实施特定的干预措施。
我们的研究目标是在没有主要临床行为障碍的受试者中，建立诊断与非约束性运动行为之间的相关性。
我们在受试者家中使用脚踝佩戴的三轴加速度计传感器，对 23 对患有 AD 痴呆症的伴侣和认知健康的伴侣进行日常运动行为研究。我们确定了从由包络检测器计算的信号包络中获得的频率特征，该检测器的载波带为 0.5 Hz 至 5 Hz。基于这些特征，我们采用二次判别分析建立区分 AD 患者和健康对照者的模型。
经过留一法交叉验证，运动特征的分类准确率达到 91%，优于基于科恩-曼斯菲尔德激越量表（CMAI）的分类准确率。运动特征与 MMSE 和 CMAI 评分显著相关。
我们的研究结果表明，即使在 AD 中没有主要的临床行为障碍，加速度计行为方案也能检测到日常行为的变化。
