从手指敲击测量预测MMSE评分
Predicting MMSE Score from Finger-Tapping Measurement
摘要：
痴呆症是老年人疾病的主要原因。早期诊断对老年痴呆症患者非常重要。在本文中，我们提出了一种痴呆诊断的方法，通过预测MMSE分数从手指敲击测量与机器学习管道。该流水线基于手指敲击运动的测量，首先利用Copula熵选择手指敲击属性，然后利用预测模型从所选择的属性中预测MMSE得分。对真实数据的实验表明，所建立的预测模型具有良好的预测性能。作为副产品，某些手指敲击属性（双手同相任务的“敲击次数”，“平均间隔”和“敲击频率”）和MMSE评分之间的关联被发现与copula熵，这可以被解释为认知能力和运动能力之间的生物学关系，因此使得预测模型可解释。所选择的手指敲击属性可以被认为是痴呆生物标志物。
英文摘要：
Dementia is a leading cause of diseases for the elderly. Early diagnosis is very important for the elderly living with dementias. In this paper, we propose a method for dementia diagnosis by predicting MMSE score from finger-tapping measurement with machine learning pipeline. Based on measurement of finger tapping movement, the pipeline is first to select finger-tapping attributes with copula entropy and then to predict MMSE score from the selected attributes with predictive models. Experiments on real world data show that the predictive models such developed present good prediction performance. As a byproduct, the associations between certain finger-tapping attributes ('Number of taps', 'Average of intervals', and 'Frequency of taps' of both hands of bimanual in-phase task) and MMSE score are discovered with copula entropy, which may be interpreted as the biological relationship between cognitive ability and motor ability and therefore makes the predictive models explainable. The selected finger-tapping attributes can be considered as dementia biomarkers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
