Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection.
Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection.
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DOI：
10.1186/1475-925x-6-23
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发表时间：
2007-06-26
影响因子：
3.9
通讯作者：
Moroz, Irene M.
中科院分区：
文献类型：
--
作者：
Little, Max A.;McSharry, Patrick E.;Roberts, Stephen J.;Costello, Declan A. E.;Moroz, Irene M.
嗓音障碍对患者影响深远，声学工具可能会客观地测量声音功能。无序的持续元音表现出广泛的现象，从几乎周期性的到高度复杂的，非周期性的振动，以及增加的“气息”。建模和替代数据研究表明，这些声音具有显著的非线性和非高斯随机特性。然而，现有的工具仅限于分析显示接近周期性的声音，并且没有考虑到这种固有的生物物理非线性和非高斯随机性，通常使用对这些特性不敏感的线性信号处理方法。它们没有直接测量紊乱的两个主要生物物理症状：复杂的非线性非周期性，以及湍流、空气声学、非高斯随机性。通常，这些工具不能应用于更严重的紊乱声音，限制了它们的临床用途。本文介绍了两种新的语音分析工具：递归和分形标度，通过直接处理这两种紊乱症状，克服了现有工具的范围限制，共同再现了声音嘶哑图。然后，一个简单的自举分类器使用这两个特征来区分正常和混乱的声音。在具有多种发音障碍的大型数据库上，使用二次判别分析，这些新技术可以区分正常和紊乱病例，总体正确分类性能为91.8±2.0%。真阳性分类性能为95.4±3.2%，真负分类性能为91.5±2.3%(95%置信度)。研究表明，这比最流行的经典工具的所有组合都要好。考虑到现有技术的大量任意参数和计算复杂性，这些新技术要简单得多，但只使用基本的分类技术就可以获得临床上有用的分类性能。他们通过利用无序语音信号中固有的非线性和动荡的随机性来做到这一点。它们被设计成广泛适用于各种杂乱无章的声音现象。因此，这些新措施可以用于各种实际的临床目的。
Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness. This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices. On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8 ± 2.0%. The true positive classification performance is 95.4 ± 3.2%, and the true negative performance is 91.5 ± 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools. Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.