New Feature Extraction from Electroglottographic Signals Applied to Automatic Detection of Laryngeal Pathologies

Alonso-Hernandez, Jesus B.; Barragán-Pulido, María Luisa; Gonzalez-Torres, Jose P.; González, Carmelo Javier León; Ferrer, Miguel A.; Juan, José de León y de; Dutta, Malay Kishore; Vyas, Garima

doi:10.1109/spin.2018.8474260

Cited by 2 publications

(2 citation statements)

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“…There are some studies which have used the derivative of EGG as a complement for the EGG signal, but the recent studies and the leading models use the standard EGG signal. 55 The features used for testing the datasets in this work is same as used by Alonso-Herna´ndez et al, 31 the only difference being that the features are tested for both unvoiced and voiced glottal activity. In the work proposed by Sunil Kumar et al, 56 differentiated EGG was used as a reference for classification of voiced and unvoiced signals, whereas in this work DEGG is used only for extracting certain features of the total features being used.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…There are various types of features that can be extracted from a bio signal. 31 The features may exist either in time domain or in the frequency domain. 32 Apart from time domain and frequency domain, some features are specifically associated with a bio signal.…”

Section: Methodology/design Considerationsmentioning

confidence: 99%