Rinne Test Modified to Quantify Hearing

Vaswani, Ravi; Parikh, Leena; Udochi, Njideka; Vaswani, Surender K.

doi:10.1097/smj.0b013e31815d3d4d

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Generally, SNHL can be of three types such as neural hearing loss, sensory hearing loss and both [2,3]. The diagnosis methods for SNHL mostly involve conventional diagnostic techniques such as Weber test and Rinne test [4]. Research on SNHL [5,6] show that it degenerates the brain tissues and this atrophy can easily be detected from the brain magnetic resonance (MR) images.…”

Section: Introductionmentioning

confidence: 99%

MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection

Nayak

Zhang

Das

et al. 2019

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection

Nayak

Zhang

Das

et al. 2019

Applied Soft Computing

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“…Оскільки в нормі повітряна провідність триваліша за кісткову, звук по повітрю буде ще чутний -тест Рінне позитивний (R+) (це може спостерігатися також і при ураженні звукосприймаючого апарату, проте тривалість сприйняття знижується). Якщо тривалість сприйняття звуку через кістку більше, ніж через повітря (стан, коли після припинення сприйняття звуку за допомогою кісткової провідності пацієнт не сприймає звук по повітрю), це свідчить про ураження звукопровідного апарату (кондуктивна приглухуватість) -тест Рінне негативний (R-) [1,3,5,11,12,17,19,21,25 Тест Швабаха (Sch) -метод діагностики сенсоневральної та кондуктивної приглухуватості. Заряджений камертон С128 встановлюють на соскоподібний відросток пацієнта.…”

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Optimization of Approaches to Early Diagnosis of Hearing Impairment in Primary Care

Maryna¹,

Olexandr²

2022

FMEP

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Almost 500 million people worldwide suffer from hearing loss or impairment. The causes of hearing loss are very diverse. Hearing loss can be caused by hereditary and non-hereditary genetic factors, certain complications during pregnancy and delivery, infectious and chronic diseases, head or ear trauma, etc. A common problem today is hearing impairment. Development of diagnostic algorithm for hearing disorders in primary care will improve the quality of diagnosis, optimize the patient’s management and ensure the earliest possible treatment and prevention measures, which will positively effect the results of treatment and patients quality of life. Primary care physicians use the ICPC-2 classification in their practice. They collect complaints, anamnesis, and test hearing ability. There are various methods to examine auditory function, most of which are based on the patient’s subjective data about perception of the heard sounds. These include perception of whispered and spoken speech, tuning fork experiments (accumetry), audiometry. Hearing is considered normal if a whispered speech is perceived at a distance of 6 m. The whispered speech degree of perception is the greatest distance from which the patient correctly repeats the words spoken by the doctor fro three times. Primary care physicians conduct a tuning fork examination of the patient using the Weber test (W), Rinne test (R), Schwabach test (Sch). Timely hearing examination with the help of modern diagnostic methods in primary care and the correct assessment of the potential capabilities of the auditory system of patients allows correct evaluation of its condition. The normal functioning of the hearing organ is one of the crucial factors for the overall development and healthy human life. After all, thanks to the auditory analyzer, a person receives information about the surrounding world.

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Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization

Chen

Yang

Chen

et al. 2016

Multimed Tools Appl

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Rinne Test Modified to Quantify Hearing

Cited by 4 publications

References 4 publications

MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection

MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection

Optimization of Approaches to Early Diagnosis of Hearing Impairment in Primary Care

Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization

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