Introduction

Adductor-type spasmodic dysphonia is a task-specific focal dystonia characterized by involuntary laryngeal muscle spasms. Due to the lack of quantitative instrumental tools, voice assessment in patients with adductor-type spasmodic dysphonia is mainly based on qualitative neurologic examination. We evaluated patients with cepstral analysis and specific machine-learning algorithms and compared the results with those collected in healthy subjects. In patients, we also used cepstral analysis and machine-learning algorithms to investigate the effect of botulinum neurotoxin type A.

Methods

We investigated 60 patients affected by adductor-type spasmodic dysphonia before botulinum neurotoxin type A therapy and 60 age and gender-matched healthy subjects. A subgroup of 35 patients was also evaluated after botulinum neurotoxin type A therapy. We recorded the sustained emission of a vowel and a sentence by means of a high-definition audio recorder. Voice samples underwent cepstral analysis as well as machine-learning algorithm classification techniques.

Results

Cepstral analysis was able to differentiate between healthy subjects and patients, but receiver operating characteristic curve analysis demonstrated that machine-learning algorithms achieved better results than cepstral analysis in differentiating healthy subjects and patients affected by adductor-type spasmodic dysphonia. Similar results were obtained when differentiating patients before and after botulinum neurotoxin type A therapy. Cepstral analysis and machine-learning measures correlated with the severity of voice impairment in patients before and after botulinum neurotoxin type A therapy.

Conclusions

Cepstral analysis and machine-learning algorithms are new tools that offer meaningful support to clinicians in the diagnosis and treatment of adductor-type spasmodic dysphonia.

中文翻译：

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内收肌痉挛性发声障碍的语音分析：客观诊断和对肉毒杆菌毒素的反应。

介绍

收肌型痉挛性肌张力障碍是一种任务特定的局灶性肌张力障碍，其特征为不自主的喉肌痉挛。由于缺乏量化的仪器工具，内收型痉挛性听觉障碍患者的语音评估主要基于定性神经系统检查。我们使用倒谱分析和特定的机器学习算法对患者进行了评估，并将结果与​​健康受试者中收集的结果进行了比较。在患者中，我们还使用了倒谱分析和机器学习算法来研究A型肉毒杆菌神经毒素的作用。

方法

我们调查了60例A型肉毒杆菌神经毒素治疗前受内收型痉挛性肌张力障碍影响的患者和60位年龄和性别匹配的健康受试者。A型肉毒杆菌神经毒素治疗后还评估了35个患者的亚组。我们通过高清音频记录器记录了元音和句子的持续发出。语音样本经过倒频谱分析以及机器学习算法分类技术。

结果

倒谱分析能够区分健康受试者和患者，但是接收器操作特征曲线分析表明，在区分健康受试者和内收型痉挛性肌张力障碍患者方面，机器学习算法比倒谱分析取得了更好的结果。当区分A型肉毒杆菌神经毒素治疗前后的患者时，可获得相似的结果。频谱分析和机器学习方法与A型肉毒杆菌神经毒素治疗前后患者语音障碍的严重程度相关。

结论

倒谱分析和机器学习算法是新工具，可为临床医生在内收型痉挛性肌张力障碍的诊断和治疗中提供有意义的支持。