Little Max A, McSharry Patrick E, Hunter Eric J, Spielman Jennifer, Ramig Lorraine O
Systems Analysis, Modelling and Prediction Group, University of Oxford, UK.
IEEE Trans Biomed Eng. 2009 Apr;56(4):1015. doi: 10.1109/TBME.2008.2005954.
We present an assessment of the practical value of existing traditional and non-standard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, Pitch Period Entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected 10 highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that non-standard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected non-standard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well-suited to telemonitoring applications.
我们通过检测发声障碍来评估现有传统和非标准测量方法在区分健康人与帕金森病（PD）患者方面的实用价值。我们引入了一种新的发声障碍测量方法，即基音周期熵（PPE），它对许多无法控制的混杂效应具有鲁棒性，包括嘈杂的声学环境以及语音频率的正常健康变化。我们收集了31人的持续发声，其中23人患有PD。然后我们选择了10个高度不相关的测量方法，通过对这些测量方法的所有可能组合进行详尽搜索，发现其中四种方法结合使用核支持向量机时，总体正确分类性能达到91.4%。总之，我们发现非标准方法与传统谐波噪声比相结合最能区分健康人和PD患者。所选的非标准方法对声学环境和个体受试者的许多无法控制的变化具有鲁棒性，因此非常适合远程监测应用。
