Smartphone Based Audiometric Test for Confirming the Level of Hearing; Is It Useable in Underserved Areas?

Renda, Levent; Selçuk, Ömer Tarık; Eyigör, Hülya; Osma, Üstün; Yılmaz, Mustafa Deniz

doi:10.5152/iao.2016.1421

Cited by 30 publications

(62 citation statements)

References 19 publications

(20 reference statements)

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“…Derin et al [2016] reported that Ear Trumpet TM was a compact and simple mobile application that can measure hearing loss with reliable results when used on the Apple iPhone 5. Renda et al [2016] evaluated in 100 adult patients the Hearing Test TM application that we used in our study. Their validity analysis showed that the results for each ear and each frequency were excellent and the absolute difference was < 8.8 dB.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Hearing Thresholds by Using a Mobile Application in Children with Otitis Media with Effusion

et al. 2020

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Objective: Otitis media with effusion (OME) is the most common cause of hearing loss in children. Early diagnosis is important as hearing loss affects speech and language development in children. The aim of this study was to compare conventional audiometry with the Android mobile operating system application Hearing Test TM in the evaluation of hearing thresholds in children with OME and to determine the accuracy and reliability of the mobile application. Design and Study Sample: Fifty school-age children aged between 5 and 15 years with OME in at least 1 ear were included in the study. First, hearing thresholds were obtained by conventional audiometric methods and the degree of hearing loss was determined. Then, the hearing thresholds of the patients were measured using the smartphone-based Hearing Test TM application. The data were compared using Cohen's kappa analysis. Results: OME was detected in 88 ears. There was no statistically significant correlation between the hearing threshold results obtained with the mobile phone and conventional audiometry at 500, 1,000, 2,000, and 4,000 Hz. Conclusion: The Android mobile phone application Hearing Test TM (version 1.1.3) is not an appropriate screening test to detect hearing loss in children with OME.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Hearing Thresholds by Using a Mobile Application in Children with Otitis Media with Effusion

et al. 2020

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“…es un método valioso para evaluar la pérdida auditiva en entornos con acceso limitado al personal de audiología [9], sin embargo, la necesidad de una audiometría más accesible y la ubicuidad de los dispositivos portátiles han fomentado el desarrollo de una variedad de aplicaciones móviles para la evaluación de la pérdida auditiva [5], [7], [10], [11]. Aunque estas nuevas aplicaciones son ampliamente accesibles y pueden ser utilizados por individuos sin formación formal, pocos han sido validados científicamente.…”

Section: Siiprin-citegcunclassified

“…De revista científica [19], [20], [9], [21], [22], [23], [24], [25], [6], [14], [26], [27], [11], [28], [29], [4], [30], [5], [31], [32], [12], [13], [33], [34], [35], [36] De conferencia [37], [38] Manuscrito [39] El protocolo de investigación más utilizado es el estudio de caso, el 69% (n=20) de los estudios primarios que utilizan este protocolo hacen referencia a aplicaciones móviles…”

Section: Artículo Estudios Seleccionadosunclassified

“…Ciencia del diseño [17], [20], [21], [24], [26], [11], [34], [35] Estudio de caso [16], [8], [18], [19], [22], [5], [13], [23], [10], [3], [27], [4], [28], [29], [12], [30], [31], [32], [36], [25] Revisión de literatura [33] Tomando como referencia el período comprendido entre los años 2003 y 2018, tiempo durante en el cual se desarrollaron y evaluaron aplicaciones móviles para diagnóstico de pérdida auditiva, se encontró que del total de estudios seleccionados: el 21% se publicaron en el 2016, el 24% se publicaron en el 2017 y el 17% se publicaron hasta mayo del 2018, años en los que se presentaron un mayor número de publicaciones, con estudios de caso de aplicaciones móviles disponibles en el mercado online y, publicaciones donde se desarrollaron nuevas aplicaciones para el diagnóstico de pérdida auditiva, que no han sido comercializadas, en la Figura 2 se puede observar la distribución de artículos por año de publicación.…”

Section: Contribución Estudios Seleccionadosunclassified

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Methods used in mobile applications for the diagnosis of hearing loss: A systematic mapping study

Leonidas

Paulina

Ruben

et al. 2020

KEG

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Hearing loss is one of the most common health problems today, it can appear at any age and the causes are varied, in order to prevent it or adapt to the changes brought about by the hearing impairment, it is necessary to diagnose it in time. The technology in terms of applications for health care smartphones has constantly evolved, so that today play an important role and are among the most downloaded from application stores, several of these applications are the diagnosis of hearing loss and use the method of pure tones. In this study a Systematic Mapping of Literature SMS (Systematic Mapping Study) is made to look for mobile applications that use other diagnostic methods that offer similar or better results, of the 13 applications found, 11 used the method of pure tones and in only 2 of them was implemented the speech audiometry (word recognition), concludes that diagnostic hearing loss tests based on mobile applications are reliable alternatives to conventional audiometric systems, and that pure tone thresholds alone are an incomplete assessment of hearing, and there is a need to develop new hearing measurement methods and combine them with other methods to complement the diagnosis. Resumen: La pérdida de la audición es uno de los problemas de salud más comunes en la actualidad, puede aparecer a cualquier edad y las causas son variadas, para poder prevenirla o adaptarse a los cambios que conlleva la deficiencia auditiva, es necesario diagnosticarla a tiempo. La tecnología en cuanto a aplicaciones para smartphones de asistencia de salud ha evolucionado constantemente, tal es así que hoy en día juegan un papel importante y son de las más descargadas de las tiendas de aplicaciones, varias de esas aplicaciones son las de diagnóstico de pérdida auditiva y utilizan el método de los tonos puros. En este estudio se hace un Mapeo Sistemático de Literatura SMS (Systematic Mapping Study) para buscar aplicaciones móviles que utilicen otros métodos de diagnóstico que ofrezcan similares o mejores resultados, de las 13 aplicaciones encontradas, 11 utilizaron el método de los tonos puros y en solo 2 de ellas se implementó la logoaudiometria (reconocimiento de palabras), por lo que se concluye que las pruebas de diagnóstico de pérdida auditiva basadas en aplicaciones móviles, son alternativas confiables a los sistemas de audiometría convencionales, y que los umbrales de tonos puros por sí solos son una evaluación incompleta de la audición, y existe la necesidad de desarrollar nuevos métodos de medición de audición y combinarlos con otros métodos para complementar el diagnóstico.

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“…In recent literature, researchers have proposed several automated hearing loss testing methods [31,32,33,34,35]. The main objective of the mentioned literature was to accurately diagnose the hearing impairment by minimizing the absolute error rate and maximizing the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing

et al. 2019

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This paper presents a novel idea that automatically identifies hearing impairment based on a cognitively-inspired feature extraction and speech recognition approach. To the best of our knowledge, this is a first attempt to automate pure tone and speech audiometry testing. Background: Hearing loss, a partial or total inability to hear, is one of the most commonly reported disabilities. A hearing test can be carried out by the audiologist to assess the patient's auditory system. However, this procedure normally requires an appointment with the audiologist, with potentially long delays and a practitioner fee. In addition, other possible problems include unavailability of required equipment and qualified practitioners, particularly in remote areas. Methods: In the proposed method, the user is asked to repeat words uttered by the machine. User response is first captured through the speech signal, and the system identifies right and wrong guesses uttered by the user, to generate an audiogram and speech recognition threshold automatically. The proposed system uses an adaptive filterbank with weighted Mel frequency cepstral coefficients (MFCCs) for feature extraction. The adaptive filterbank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Contrary to the state-of-the-art static MFCCs, the cognitive feature extraction method senses the spectrum in order to design the adaptive filterbank of relevant frequency bands. A number of machine learning based classification algorithms are finally employed, including the support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and Hidden Markov Model (HMM). Results: Comparative performance evaluation demonstrates the potential of our automated hearing test method to achieve comparable results to the clinical ground truth, established by the expert audiologist's tests. The overall absolute error of the proposed model when compared with the expert audiologist test is less than 4.9 dB and 4.4 dB for the pure tone and speech audiometry tests respectively, achieving accuracy up to 96.67% using HMM. Conclusion: The proposed method could potentially offer a second opinion to audiologists, and serve as a cost-effective pre-screening test to predict hearing loss at an early stage. Currently, we are exploring the application of advanced deep learning and optimization approaches to further enhance the performance of the automated testing prototype. Noname manuscript No.

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Smartphone Based Audiometric Test for Confirming the Level of Hearing; Is It Useable in Underserved Areas?

Cited by 30 publications

References 19 publications

Evaluation of Hearing Thresholds by Using a Mobile Application in Children with Otitis Media with Effusion

Evaluation of Hearing Thresholds by Using a Mobile Application in Children with Otitis Media with Effusion

Methods used in mobile applications for the diagnosis of hearing loss: A systematic mapping study

Cognitively Inspired Feature Extraction and Speech Recognition for Automated Hearing Loss Testing

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