KSG estimation of reconstruction delay to detect vocal disorders in nonlinear dynamical analysis

Abstract Introduction This research investigates the applicability of a relatively new estimator of mutual information, KSG estimator, to find the reconstruction delay of phase space in dynamical systems. There are evidences that the KSG estimator is more accurate than the naive method commonly used. Methods In this paper we estimated mutual information between the voice signals and their delayed versions, with KSG method. The voice signals were obtained from a disordered voice database. Then, we found the reconstruction delay where mutual information reached its first minimum. We applied the encountered value of reconstruction delay in linear discriminant analysis, in order to discriminate between healthy and pathological voices or to discriminate between pathologies. Discrimination between voice pathologies using nonlinear measurements is still not much explored. Moreover, in this paper we used a single nonlinear measurement: reconstruction delay. Results The results show that the reconstruction delay obtained with KSG method has increased classification rates in most cases, in terms of accuracy, sensitivity and specificity, when compared to the naive estimator usually adopted. Conclusion The KSG estimator is a promising technique to improve the diagnosis of voice related pathologies.

摘要 引言
本研究旨在探究一种较为新颖的互信息（mutual information）估计器——KSG估计器（KSG estimator）在动力学系统相空间重构延迟求解中的适用性。已有研究表明，KSG估计器相较于常用的朴素方法具备更高的准确性。
方法
本文采用KSG方法估算语音信号与其延迟版本之间的互信息。实验所用语音信号取自某病理性语音数据库。随后，我们确定了互信息首次达到极小值时对应的重构延迟。将所得重构延迟值应用于线性判别分析，以区分健康语音与病理性语音，或区分不同类型的病理语音。采用非线性测量手段区分语音病理的相关研究尚不多见，且本文仅采用了单一非线性测量指标：重构延迟。
结果
相较于常规采用的朴素估计器，采用KSG方法得到的重构延迟在多数场景下均可提升分类任务的准确率、敏感性与特异性。
结论
KSG估计器是一项颇具潜力的技术，可用于优化与语音相关病理的诊断工作。