Voice Disorder Detection via an m-Health System: Design and Results of a Clinical Study to Evaluate Vox4Health.
Voice Disorder Detection via an m-Health System: Design and Results of a Clinical Study to Evaluate Vox4Health.
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DOI：
10.1155/2018/8193694
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发表时间：
2018
影响因子：
--
通讯作者：
Verde L
中科院分区：
文献类型：
--
作者：
Cesari U;De Pietro G;Marciano E;Niri C;Sannino G;Verde L
当前的研究对Vox 4Health进行了临床评估，Vox 4Health是一种移动健康系统，能够通过计算和分析声学分析所需的主要声学指标（即基频、抖动、闪烁和谐波）来估计可能存在的语音障碍。噪声比。声学分析是一种客观、有效、非侵入性的工具，用于临床实践中对语音质量进行定量评估。 与那不勒斯费德里科二世大学的医务人员合作进行了一项临床研究。共招募208名志愿者（平均年龄，44.2 ± 13.9岁），58名健康受试者（平均年龄，36.7 ± 13.3岁）和150名病理受试者（平均年龄，47 ± 13.1岁）。Vox4Health的评估是根据分类性能进行的，即，灵敏度、特异性和准确性，通过使用基于规则的算法，该算法考虑最具特征的声学参数来分类声音是健康的还是病态的。的性能进行了比较，通过使用Praat，在临床实践中最常用的工具之一。 使用基于规则的算法，在语音障碍的检测，72.6%，最好的准确性，通过使用抖动或微光值。此外，最佳灵敏度约为96%，并且总是通过使用抖动来获得。最后，通过使用基频实现了最佳特异性，其等于56.9%。此外，为了提高下一版本Vox4Health应用程序的分类准确性，使用机器学习技术进行了评估。我们采用不同的机器学习技术进行了一些初步测试，能够将声音分类为健康或病态。Logistic模型树算法获得了最好的准确性（77.4%），而最好的灵敏度（99.3%）是使用支持向量机。最后，基于实例的学习表现出最好的特异性（36.2%）。 考虑到所实现的准确性，Vox4Health已被医学专家认为是当前版本中检测语音障碍的“良好筛查工具”。然而，当考虑机器学习分类器而不是基于规则的算法时，这种准确性得到了提高。
The current study presents a clinical evaluation of Vox4Health, an m-health system able to estimate the possible presence of a voice disorder by calculating and analyzing the main acoustic measures required for the acoustic analysis, namely, the Fundamental Frequency, jitter, shimmer, and Harmonic to Noise Ratio. The acoustic analysis is an objective, effective, and noninvasive tool used in clinical practice to perform a quantitative evaluation of voice quality. A clinical study was carried out in collaboration with medical staff of the University of Naples Federico II. 208 volunteers were recruited (mean age, 44.2 ± 13.9 years), 58 healthy subjects (mean age, 36.7 ± 13.3 years) and 150 pathological ones (mean age, 47 ± 13.1 years). The evaluation of Vox4Health was made in terms of classification performance, i.e., sensitivity, specificity, and accuracy, by using a rule-based algorithm that considers the most characteristic acoustic parameters to classify if the voice is healthy or pathological. The performance has been compared with that achieved by using Praat, one of the most commonly used tools in clinical practice. Using a rule-based algorithm, the best accuracy in the detection of voice disorders, 72.6%, was obtained by using the jitter or shimmer value. Moreover, the best sensitivity is about 96% and it was always obtained by using jitter. Finally, the best specificity was achieved by using the Fundamental Frequency and it is equal to 56.9%. Additionally, in order to improve the classification accuracy of the next version of the Vox4Health app, an evaluation by using machine learning techniques was conducted. We performed some preliminary tests adopting different machine learning techniques able to classify the voice as healthy or pathological. The best accuracy (77.4%) was obtained by the Logistic Model Tree algorithm, while the best sensitivity (99.3%) was achieved using the Support Vector Machine. Finally, Instance-based Learning performed the best specificity (36.2%). Considering the achieved accuracy, Vox4Health has been considered by the medical experts as a “good screening tool” for the detection of voice disorders in its current version. However, this accuracy is improved when machine learning classifiers are considered rather than the rule-based algorithm.
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影响因子：
2.6
作者：
Dejonckere, PH;Bradley, P;Woisard, V
通讯作者：
Woisard, V
影响因子：
7.5
作者：
Landwehr, N;Hall, M;Frank, E
通讯作者：
Frank, E
影响因子：
2.1
作者：
Baki, Mat M.;Wood, G.;Birchall, M. A.
通讯作者：
Birchall, M. A.
影响因子：
2.4
作者：
de Cheveigné, A;Kawahara, H
通讯作者：
Kawahara, H
影响因子：
2.2
作者：
Lowell, Soren Y.;Colton, Raymond H.;Hahn, Youngmee C.
通讯作者：
Hahn, Youngmee C.