基于语音扰动和振动分析的球型ALS检测
Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato
摘要：
平均而言，缺乏生物标志物会导致一年的诊断延迟，以检测肌萎缩侧索硬化症（ALS）。为了改善诊断过程，可以使用基于声学分析的自动语音评估。本研究的目的是验证持续元音发声测试在ALS患者自动检测中的适用性。我们提出了增强的程序分离的语音信号的基本周期，需要计算的扰动测量（如抖动和闪烁）。同时，我们也提出了定量评估持续元音发声中病理性颤音表现的方法。实验结果表明，采用本文提出的声学分析方法，基于线性判别分析的分类器的准确率为90.7%，灵敏度为86.7%，特异度为92.2%。
英文摘要：
On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of pathological vibrato manifestations in sustain vowel phonation. The study's experiments show that using the proposed acoustic analysis methods, the classifier based on linear discriminant analysis attains 90.7\% accuracy with 86.7\% sensitivity and 92.2\% specificity.
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