Introductory Chapter - Genealogy of Audiology

Hatzopoulos, Stavros

doi:10.5772/67437

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Nowadays, individuals may suffer from various types of diseases, including hearing disorders which may or do occur at any time in a person’s life, including infants and children. In 2018, about 14.9% of school-age children were estimated to have hearing loss [ 22 , 39 ]. In most cases, children older than 3 years of age suffer permanent hearing loss which had not been identified earlier.…”

Section: Introductionmentioning

confidence: 99%

Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

Hamid

Sallehuddin

Yunos

et al. 2023

Neural Comput & Applic

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Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.

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Section: Introductionmentioning

confidence: 99%

Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

Hamid

Sallehuddin

Yunos

et al. 2023

Neural Comput & Applic

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Speech auditory brainstem responses (s-ABRs) as a new approach for the assessment of speech sounds coding

Elsayed,

Nada,

Galhoum

et al. 2024

Egypt J Otolaryngol

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Background The evoked auditory brainstem response (EABR) is an objective electrophysiological test used to assess the brainstem’s auditory neural activity. Speech ABR (s-ABR) testing using verbal stimuli gives more important details about how the brainstem processes speech inputs which enables the detection of auditory processing impairments that do not manifest in click-provoked ABR. The use of speech syllables in the s-ABR reveals an important brainstem function that plays a crucial part in reading development and phonologic achievement which is an assessment of speech syllables. The syllable /da/ is often utilized in s-ABR measurement being a global syllable that can be tested in many nations with effective experimental confidence. Conclusion The speech ABR is an objective, unbiased, quick test. It can be used to differentiate between many conditions such as auditory processing disorders (APD), specific language impairment (SLI), and children with academic challenges.

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Introductory Chapter - Genealogy of Audiology

Cited by 2 publications

References 8 publications

Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

Speech auditory brainstem responses (s-ABRs) as a new approach for the assessment of speech sounds coding

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