论文标题
通过轻拍测量来预测MMSE得分
Predicting MMSE Score from Finger-Tapping Measurement
论文作者
论文摘要
痴呆是老年人疾病的主要原因。早期诊断对于患有痴呆症的老年人非常重要。在本文中，我们提出了一种通过通过机器学习管道来预测手指敲击测量的MMSE评分，提出了一种痴呆诊断的方法。基于对手指敲击运动的测量，该管道首先是使用Copula熵选择手指敲击属性，然后从具有预测模型的所选属性中预测MMSE得分。现实世界数据的实验表明，这种开发的预测模型当前的预测性能良好。作为一种副产品，某些手指轻拍属性（“抽头的数量”，“间隔的平均值”和双手同相任务的双手的“水龙头频率”）和MMSE分数是用Copula Entropy发现的，这可以解释为可以解释为认知能力和运动能力和运动能力和预测模型之间的生物学关系。选定的手指敲击属性可以视为痴呆生物标志物。
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.
