Grouping Intrinsic Mode Functions and Residue for Pathological Classifications via Electroglottograms

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-04-01 DOI:10.1016/j.irbm.2022.11.001
G. Liao, B.W.-K. Ling, K.-G. Pang
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引用次数: 0

Abstract

Objectives

The electroglottogram (EGG) is a signal used for measuring the change of the relative contact area in the vocal cord during the throat production. In the recent years, the low cost and the non-invasive applications have been derived. Hence, the EGG has been applied in various science, engineering and medical fields such as in the basic voice science including the phonetics, the singing and the hearing as well as in the speech and the language therapy and the related clinical works including the voice production physiology, the swallowing and the psychology. However, the pathological classifications using the EGGs usually yield the poor performances. This is because the EGGs are required to decompose into the various components for extracting the features for performing the classifications. Nevertheless, the total numbers of the components decomposed by some time frequency representation such as the empirical mode decomposition (EMD) for different EGGs are different. Hence, the dimension of the feature vectors extracted from different EGGs is different. This introduces to the difficulty for building a machine learning model for performing the classification. This paper is to address this issue.

Material and methods

This paper proposes a method for grouping the intrinsic mode functions (IMFs) and the residue obtained by applying the EMD to the EGGs for classifying between the healthy subjects and the pathological subjects. More precisely, this paper proposes a clustering based method to group the IMFs and the residue so that the total numbers of the grouped IMFs of different EGGs are the same. First, the IMFs and the residue of the EGGs are categorized into a desired number of groups based on their correlation coefficients. Second, the IMFs or the residue in each group are summed together to obtain the grouped IMF. Third, the mean frequency and the first formant of each grouped IMF are computed. Finally, a random forest is employed for performing the classification. To our best knowledge, this joint EMD and clustering based method is firstly proposed to preform the pathological voice detection. The computer numerical simulations are conducted using the online available Saarbrücken voice database.

Results

Here, five cross validations have been performed. The mean accuracy, the mean specificity and the mean sensitivity among these five validations are 86.98, 79.92 and 91.57, respectively. The standard deviation of the accuracy, the specificity and the sensitivity among these five validations are ±2.00%, ±3.71% and ±2.13%, respectively. The simulation results show that our proposed method outperforms the common EGG or speech processing based methods.

Conclusion

This paper proposes a clustering based method for grouping the IMFs and the residue for performing the pathological classifications via the EGGs. The grouping criterion is based on the correlation coefficients. It is found that our proposed method can achieve the highest classifications for the majority signal to noise ratios compared to the state of the arts methods.

Abstract Image

通过声门电图分组本征模式功能和残差进行病理分类
目的声门电图(EGG)是一种用于测量喉咙生产过程中声带相对接触面积变化的信号。近年来,低成本和非侵入性的应用已经出现。因此,EGG已被应用于各种科学、工程和医学领域,如语音基础科学,包括语音、歌唱和听力,语音和语言治疗以及相关的临床工作,包括语音产生生理学、吞咽和心理学。然而,使用EGG的病理分类通常产生较差的表现。这是因为需要将EGG分解为各种组件,以提取用于执行分类的特征。然而,对于不同的EGG,通过一些时间频率表示(例如经验模式分解(EMD))分解的分量的总数是不同的。因此,从不同的EGG提取的特征向量的维度是不同的。这介绍了建立用于执行分类的机器学习模型的困难。这篇论文就是为了解决这个问题。材料和方法本文提出了一种将EMD应用于EGG获得的固有模函数(IMF)和残差分组的方法,用于在健康受试者和病理受试者之间进行分类。更准确地说,本文提出了一种基于聚类的方法来对IMF和残差进行分组,以使不同EGG的分组IMF的总数相同。首先,根据其相关系数,将IMF和EGG的残差分类为所需数量的组。其次,将每组中的IMF或残差相加在一起,以获得分组的IMF。第三,计算每个分组IMF的平均频率和第一共振峰。最后,采用随机森林进行分类。据我们所知,这种基于EMD和聚类的联合方法首次被提出用于病理语音检测。计算机数值模拟是使用在线可用的萨尔布吕肯语音数据库进行的。结果在这里,进行了五次交叉验证。这五种验证的平均准确度、平均特异性和平均灵敏度分别为86.98、79.92和91.57。这五种验证的准确度、特异性和灵敏度的标准偏差分别为±2.00%、±3.71%和±2.13%。仿真结果表明,我们提出的方法优于常见的基于EGG或语音处理的方法。结论本文提出了一种基于聚类的IMF和残差分组方法,用于通过EGG进行病理分类。分组标准基于相关系数。发现与现有技术的方法相比,我们提出的方法可以实现大多数信噪比的最高分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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