论文标题
通过发音的声学分析评估帕金森氏病的进度
Assessing Progress of Parkinson s Disease Using Acoustic Analysis of Phonation
论文作者
论文摘要
本文介绍了帕金森氏病（PD）患者的发音的复杂声学分析，特别关注疾病进步的估计，这是7种不同的临床量表所描述的。 g。统一的帕金森氏病评级量表或贝克抑郁症清单。该分析基于84例PD患者发音的5个捷克元音的参数化。使用分类和回归树，我们估计所有临床评分，最大误差较低或等于13％。在小型日期化检查的情况下观察到了最佳估计（MAE = 0.77，估计错误5.50％。最后，我们提出了基于随机森林的二进制分类，该二进制分类能够鉴定出具有敏感性SEN = 92.86％的帕金森氏病（SPE = 85.71％）。参数参数过程是基于107个语音的量化量的量身定量的，该临床的提取量是量身定量的。
This paper deals with a complex acoustic analysis of phonation in patients with Parkinson's disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales ,e. g. Unified Parkinson's disease rating scale or Beck depression inventory. The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %. Finally, we proposed a binary classification based on random forests that is able to identify Parkinson's disease with sensitivity SEN = 92.86 % (SPE = 85.71 %). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD.
