[结合语音样本与特征双边选择算法用于帕金森病分类]
[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].
作者信息
Zhang Xiaoheng, Wang Lirui, Cao Yao, Wang Pin, Zhang Cheng, Yang Liuyang, Li Yongming, Zhang Yanling, Cheng Oumei
机构信息
Chongqing Radio & TV University, Chongqing 400052, P.R.China.
College of Communication Engineering, Chongqing University, Chongqing 400030,P.R.China.
出版信息
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Feb 1;34(6):942-948. doi: 10.7507/1001-5515.201704061.
Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.
近年来，基于语音数据诊断帕金森病（PD）已被证明是一种有效的方法。然而，目前的研究只关注特征提取和分类器设计，而没有考虑实例选择。作者之前的研究表明，实例选择可以提高分类准确率。然而，到目前为止，尚未有人关注语音样本与特征之间的关系。因此，本文提出了一种新的PD诊断算法，该算法基于相关特征加权算法和多核方法同时选择语音样本和特征，以发现它们的协同效应，从而提高分类准确率。实验结果表明，该算法在分类准确率上有显著提高。它可以获得82.5%的平均分类准确率，比相关算法高出30.5%。此外，该算法检测到了语音样本和特征的协同效应，这对语音标记提取具有重要价值。
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