Relation of RBH Auditory-Perceptual Scale to Acoustic and Electroglottographic Voice Analysis in Children With Vocal Nodules

Szklanny, Krzysztof; Wrzeciono, Piotr

doi:10.1109/access.2019.2907397

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…Statistical analysis revealed an overall low and neglectable influence of subject age. VRQOL, I max , F max and Jit(%) were found to be the most reliable parameter subset for differentiating between groups N 01 , FD 01 and FD 23 . Furthermore those parameters also reflected changes between pre-and post-treatment groups.…”

Section: A Summarymentioning

confidence: 94%

“…S2). Whilst the addition of neither F min , nor I max to the parameter set yielded a distinct increase in AUC or ACC, both contributed by decreasing the difference between sensitivity and specificity, mainly for the male group comparison FD 01 vs. FD 23 . Males have a lower speaking fundamental frequency than females [60] and whilst F max was more important to identify voice disorder in females (see Fig.…”

Section: Model Selection and Optimizationmentioning

confidence: 94%

“…1) N 01 vs. FD 01 2) N 01 vs. FD 23 3) FD 01 vs. FD 23 Four different boosted trees algorithms were investigated using the MATLAB function ''fitcensemble'', namely AdaBoost, LogitBoost, GentleBoost and RUSBoost. We included AdaBoost as it is one of the most popular algorithms in this field [51] and hence a common choice.…”

Section: B Model Selection and Optimizationmentioning

confidence: 99%

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Determination of Clinical Parameters Sensitive to Functional Voice Disorders Applying Boosted Decision Stumps

Schlegel

Kist

Semmler

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Background: Various voice assessment tools, such as questionnaires and aerodynamic voice characteristics, can be used to assess vocal function of individuals. However, not much is known about the best combinations of these parameters in identification of functional dysphonia in clinical settings. Methods: This study investigated six scores from clinically commonly used questionnaires and seven acoustic parameters. 514 females and 277 males were analyzed. The subjects were divided into three groups: one healthy group (N 01 ) (49 females, 50 males) and two disordered groups with perceptually hoarse (FD 23 ) (220 females, 96 males) and perceptually not hoarse (FD 01 ) (245 females, 131 males) sounding voices. A tree stumps Adaboost approach was applied to find the subset of parameters that best separates the groups. Subsequently, it was determined if this parameter subset reflects treatment outcome for 120 female and 51 male patients by pairwise pre-and post-treatment comparisons of parameters. Results: The questionnaire ''Voice-relatedquality-of-Life'' and three objective parameters (''maximum fundamental frequency'', ''maximum Intensity'' and ''Jitter Percent'') were sufficient to separate the groups (accuracy ranging from 0.690 (FD 01 vs. FD 23 , females) to 0.961 (N 01 vs. FD 23 , females)). Our study suggests that a reduced parameter subset (4 out of 13) is sufficient to separate these three groups. All parameters reflected treatment outcome for patients with hoarse voices, Voice-related-quality-of-Life showed improvement for the not hoarse group (FD 01 ). Conclusion: Results show that single parameters are insufficient to separate voice disorders but a set of several well-chosen parameters is. These findings will help to optimize and reduce clinical assessment time.

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Section: A Summarymentioning

confidence: 94%

Section: Model Selection and Optimizationmentioning

confidence: 94%

Section: B Model Selection and Optimizationmentioning

confidence: 99%

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Determination of Clinical Parameters Sensitive to Functional Voice Disorders Applying Boosted Decision Stumps

Schlegel

Kist

Semmler

et al. 2020

IEEE J. Transl. Eng. Health Med.

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“…EGG signal was acquired from the developed prototype, statistical features were extracted from the acquired signal and further the normal and thyroid individuals were classified based on the machine learning classifiers. 16 Szklanny et al, proposed a noninvasive diagnostic tool for assessing the vocal nodules based on a genetic algorithm. Input data used for the study is voice and EGG recording of the individuals.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers

Sai

Rajalakshmi

Snekhalatha

2021

Proc Inst Mech Eng H

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Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such as Hyperthyroidism or Hypothyroidism. Electroglottography signal is a bio signal which represents the impedance that exist between the glottis regions. The study aims at design and development of an hardware circuit for the acquisition of Electroglottogram signal from normal and thyroid subjects is proposed followed by feature extraction from the acquired bio signal is performed. Further, machine learning classifiers were used to classify the normal and thyroid individuals. This modality of acquisition is non-invasive. Performance evaluation is done by testing various classifiers to study the accuracy. The classifiers tested were Random Forest, Random Tree, Bayes Net, Multilayer Perceptron, Simple Logistic classifier, and One-R classifier. Classifiers such as Random Forest, Random Tree, and Multilayer Perceptron showed high accuracy. The accuracy estimated by these classifiers was tested and its ROC curves with AUC scores were derived. The highest accuracy was reported for Simple Logistic classifier which was about 95.1%. Random Forest and Random Tree reported 93.5% and 91.9% respectively. Similarly, Multilayer Perceptron and Bayes Net gave 93.5% and 91.9%. The One-R classifier algorithm reported the lowest accuracy of 90.3% among the studied classifier algorithms. The ROC-AUC score for the classifiers were also reported to be more than 0.9 which is considered more promising and supports the acquisition and processing methodology. Hence the proposed technique can be efficiently used to diagnose thyroid non-invasively.

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Immediate effect of recurrent laryngeal nerve stimulation in patients with idiopathic unilateral vocal fold paralysis

Yan,

Hou,

Zhang

et al. 2024

Acta Oto-Laryngologica

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Relation of RBH Auditory-Perceptual Scale to Acoustic and Electroglottographic Voice Analysis in Children With Vocal Nodules

Cited by 3 publications

References 43 publications

Determination of Clinical Parameters Sensitive to Functional Voice Disorders Applying Boosted Decision Stumps

Determination of Clinical Parameters Sensitive to Functional Voice Disorders Applying Boosted Decision Stumps

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers

Immediate effect of recurrent laryngeal nerve stimulation in patients with idiopathic unilateral vocal fold paralysis

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