Classification Of Hearing Loss Research Articles

Genetic characteristic analysis of slight-to-moderate sensorineural hearing loss in children

Objective:To analyze genetic factors and phenotype characteristics in pediatric population with slight-to-moderate sensorineural hearing loss. Methods:Children with slight-to-moderate sensorineural hearing loss of and their parents, enrolled from the Chinese Deafness Genome Project, were studied. Hearing levels were assessed using pure tone audiometry, behavioral audiometry, auditory steady state response（ASSR）, auditory brainstem response（ABR） thresholds, and deformed partial otoacoustic emission（DPOAE）. Classification of hearing loss is according to the 2022 American College of Medical Genetics and Genomics（ACMG） Clinical Practice Guidelines for Hearing Loss. Whole exome sequencing（WES） and deafness gene Panel testing were performed on peripheral venous blood from probands and validations were performed on their parents by Sanger sequencing. Results:All 134 patients had childhood onset, exhibiting bilateral symmetrical slight-to-moderate sensorineural hearing loss, as indicated by audiological examinations. Of the 134 patients, 29（21.6%） had a family history of hearing loss, and the rest were sporadic patients. Genetic causative genes were identified in 66（49.3%） patients. A total of 11 causative genes were detected, of which GJB2 was causative in 34 cases（51.5%）, STRC in 10 cases（15.1%）, MPZL2 gene in six cases（9.1%）, and USH2A in five cases（7.6%）.The most common gene detected in slight-to-moderate hearing loss was GJB2, with c. 109G>A homozygous mutation found in 16 cases（47.1%） and c. 109G>A compound heterozygous mutation in 9 cases（26.5%）. Conclusion:This study provides a crucial genetic theory reference for early screening and detection of mild to moderate hearing loss in children, highlighting the predominance of recessive inheritance and the significance of gene like GJB2, STRC, MPZL2, USH2A.

A current affair: worker perceptions of noise exposure and occupational hearing loss in Australian coal mines.

The objective of the online survey was to determine worker attitudes towards, perceptions on hearing loss, and management of workplace noise; and to identify barriers within current strategies that prevent effective management of hearing health in Australian mines. This cross-sectional study utilized a modified survey design, initially designed for use by Safe Work Australia for a broader study published in 2010. The survey questionnaire was made available online to volunteer participants, recruited with the assistance of State and National Health and Safety, and mining organizations. Volunteer participants were required to be proficient in English, be employed by an Australian underground or open cut mine, including coal processing plants; or work as a contractor on one of the specified mine sites. All mining employees, regardless of occupation, job title, and occupational hearing loss classification or status, were invited to complete the questionnaire. Almost 60% of respondents indicated that they had high noise exposure for than 10 yr or more, and have some trouble hearing, mostly associated with infrequent tinnitus. Nearly 71% of these workers believe that the noise control strategies in their workplaces are effective, but this mostly refers to the use of hearing protection devices. The results indicate that general knowledge on the cause and effect of noise exposure in the workplace is well understood. However, due to the long latency associated with the development of noise-induced hearing loss (NIHL), there is an issue urgency in terms of risk management. It is surprising that most of the respondents recommended more inspections and administrative controls, especially since most respondents were health, safety, and environment (HSE) professionals. HSE professionals should be advocating for higher order, more permanent solutions, and not purely administrative controls and personal protective equipment. These findings raise the question of whether there is a multifaceted working-culture issue that needs to be addressed, in combination with higher order control implementation.

APPSO‐NN: An adaptive‐probability particle swarm optimization neural network for sensorineural hearing loss detection

AbstractAs a hearing disorder, sensorineural hearing loss (SNHL) can be effectively detected by magnetic resonance imaging (MRI). However, the manual detection of MRI scanning is subjective, time‐consuming, and unpredictable. An accurate and automatic computer‐aided diagnosis system is proposed for SNHL detection, providing reliable references for professionals. The system first employs a wavelet entropy layer to extract features of MRI images. Then, a neural network layer is proposed as the classifier consisting of a feedforward neural network (FNN) and an adaptive‐probability PSO (APPSO) algorithm. The authors prove the rotation‐variant property of the basic particle swarm optimization (PSO) by the algebraic property of matrix transformation. The property is unsuitable for optimising parameters of neural networks. Thus, in APPSO, the authors integrate the new update rules based on all‐dimensional variation and adaptive‐probability mechanism into the basic PSO, which can improve its searching ability without losing population diversity. The authors compare APPSO‐NN with FNN trained by five popular evolutionary algorithms. The simulation results show that APPSO performs best in training FNN. The method also compares with six state‐of‐the‐art methods. The simulation results show that the best performance in sensitivity and overall accuracy of hearing loss classification, which proves that the method is effective and promising for SNHL detection.

Hearing loss classification via AlexNet and Support Vector Machine

This paper presents a new method for detecting hearing loss. Our approach is first to use AlexNet to extract the features. Then, we use the Support Vector Machine as a classifier to classify the images. 10-fold cross-validation results showed that the sensitivities of the healthy control group, the left-sided hearing loss group, and the right-sided hearing loss group in this method were 94.67%, 94.00%, and 95.17%, respectively, achieving a very good effect compared with other hearing loss detection methods. In conclusion, our method is effective for the identification of hearing loss.

Hearing loss classification algorithm based on the insertion gain of hearing aid

AbstractHearing loss is one of the most prevalent chronic health problems worldwide and a common intervention is the wearing of hearing aids. However, the tedious fitting procedures and limited hearing experts pose restrictions for the popularity of hearing aids. This paper introduced a hearing loss classification method based on the insertion gain of hearing aids, which aims to simplify the fitting procedure and achieve a fitting-free effect of the hearing aid, in line with current research trends in key algorithms for fitting-free hearing aids. The proposed method innovatively combines the insertion gain of hearing aids with the covariates of patient’s gender, age, wearing history to form a new set of hearing loss vectors, and then classifies the hearing loss into six categories by unsupervised cluster analysis method. Each category of representative parameters characterizes a typical type of hearing loss, which can be used as the initial parameter to improve the efficiency of hearing aid fitting. Compared with the traditional audiogram classification method AMCLASS (Automated Audiogram Classification System), the proposed classification method reflect the actual hearing loss of hearing impaired patients better. Moreover, the effectiveness of the new classification method was verified by the comparison between the obtained six sets of representative insertion gains and the inferred hearing personalization information.

Audiological Assessment in Post COVID19 Patients- A Cross-sectional Study

Introduction: Hearing loss following a viral infection is a common entity. In recent studies, hearing loss has been seen among Coronavirus Disease 2019 (COVID-19) infected patients, but its association is yet to be established. Aim: To determine the presence of hearing loss and its type in patients after COVID-19 infection. Materials and Methods: A cross-sectional study was conducted at a tertiary health centre, Department of Otorhinolaryngology at Chettinad Academy of Research and Education, Chettinad Hospital and Research Institute, Chennai, from October 2021 to April 2022. Total of 125 patients, who had a positive history of COVID-19 infection, were reviewed in the Otorhinolaryngology Department, one month after they were tested Real Time-Polymerase Chain Reaction (RT-PCR) positive. After obtaining proper clinical history and examination, Pure Tone Audiometry (PTA) were done. Audiological report was assessed and analysed. Qualitative variables will be expressed in proportions and quantitative variables in Mean±SD/ Median (IQR), Chi- square test was applied. Results: This study included 65 males (52%) and 60 females (48%), and the mean age was 38.44±10.9 years years. Among the 125 patients, 12 (9.6%) were diabetic, 14 (11.2%) were hypertensive, 5 (4%) had dyslipidaemia, 3 (2.4%) were hypothyroid, while remaining 91 patients (72.8%) had no comorbidities. Sensorineural Hearing Loss (SNHL) was found among 45 patients (34 with unilateral and 11 with bilateral involvement). Out of them, 2 (4.5%) (4.5%) were in the age group of 18-30 years, 19 (42.2%) in 31-45 years and 24 (53.3%) between 46-60 years age group. Based on the World Health Organization (WHO) classification of hearing loss, 27 patients had mild sensorineural hearing loss, 12 patients with moderate, and 6 patients with moderately severe sensorineural hearing loss. Conclusion: SNHL were found among patients who had COVID-19 infection, but due to the absence of a pre COVID-19 documented audiogram, it was difficult to conclude whether the hearing loss had occurred due to COVID-19, pre-existing hearing loss, or age-related. Further studies are required for proper understanding and correlation.

Audiological Assessment in Post COVID19 Patients- A Cross-sectional Study

Introduction: Hearing loss following a viral infection is a common entity. In recent studies, hearing loss has been seen among Coronavirus Disease 2019 (COVID-19) infected patients, but its association is yet to be established. Aim: To determine the presence of hearing loss and its type in patients after COVID-19 infection. Materials and Methods: A cross-sectional study was conducted at a tertiary health centre, Department of Otorhinolaryngology at Chettinad Academy of Research and Education, Chettinad Hospital and Research Institute, Chennai, from October 2021 to April 2022. Total of 125 patients, who had a positive history of COVID-19 infection, were reviewed in the Otorhinolaryngology Department, one month after they were tested Real Time-Polymerase Chain Reaction (RT-PCR) positive. After obtaining proper clinical history and examination, Pure Tone Audiometry (PTA) were done. Audiological report was assessed and analysed. Qualitative variables will be expressed in proportions and quantitative variables in Mean±SD/ Median (IQR), Chi- square test was applied. Results: This study included 65 males (52%) and 60 females (48%), and the mean age was 38.44±10.9 years years. Among the 125 patients, 12 (9.6%) were diabetic, 14 (11.2%) were hypertensive, 5 (4%) had dyslipidaemia, 3 (2.4%) were hypothyroid, while remaining 91 patients (72.8%) had no comorbidities. Sensorineural Hearing Loss (SNHL) was found among 45 patients (34 with unilateral and 11 with bilateral involvement). Out of them, 2 (4.5%) (4.5%) were in the age group of 18-30 years, 19 (42.2%) in 31-45 years and 24 (53.3%) between 46-60 years age group. Based on the World Health Organization (WHO) classification of hearing loss, 27 patients had mild sensorineural hearing loss, 12 patients with moderate, and 6 patients with moderately severe sensorineural hearing loss. Conclusion: SNHL were found among patients who had COVID-19 infection, but due to the absence of a pre COVID-19 documented audiogram, it was difficult to conclude whether the hearing loss had occurred due to COVID-19, pre-existing hearing loss, or age-related. Further studies are required for proper understanding and correlation.

Association of hearing health with traffic accidents among heavy vehicle drivers

Aim: Excessive exposure of drivers to traffic noise and the resulting hearing loss could affect safe driving. This study aimed to investigate the hearing status of heavy vehicle drivers and its correlation with traffic accidents. Materials and Methods: This cross-sectional study was conducted on 187 drivers of heavy vehicles (trucks and buses) in Tehran province during 2018. The hearing status of drivers was investigated by the audiometric test method, ISO 6189 (1983). A safety questionnaire and checklist of drivers' demographic variables were used to gather information on accident status and personal characteristics of drivers. SPSS version 24 software was used for the statistical analysis of data. The statistical tests used are Chi-square, Spearman's correlation, paired t-test, and odds ratio. Results: Examining the hearing status of drivers showed that 51.5% were normal and 48.5% of drivers had hearing loss. The highest mean hearing threshold was at a frequency of 4000 Hz. The mean (standard deviation) for the right and left ears was 27.82 ± 15.92 and 18 ± 27.32 dB, respectively. The results of the analysis on drivers' hearing status and accidents showed a significant correlation between the frequency of accidents and the classification of drivers' hearing loss. Thus, a significant relationship with the correlation coefficient of 0.123 and 0.307, respectively, was obtained between the average hearing loss of drivers and having one or five accidents. Conclusion: The findings of the study showed hearing loss in drivers in the left ear, especially at high frequencies. There is also a significant relationship between the number of accidents and the rate of hearing loss of drivers. Hearing loss caused by noise interferes with activities such as driving and negatively affects the safety of drivers and emphasizes the importance of preventing accidents by controlling traffic noise.

HEARING LOSS IN CHILDREN AFTER TYMPANIC MEMBRANE PERFORATION: CLUSTER ANALYSIS OF 27 CASES

IntroductionTympanic membrane perforation (TMP) may be caused by several factors but commonly leads to conductive hearing loss. This study aims to characterize the profiles of hearing loss in pediatric patients with TMPs.Material and methodsA retrospective analysis of the medical charts of 27 patients was conducted. Otoscopy of the TM was done and pure tone audiometry was used to assess hearing loss. Cluster analysis was applied to evaluate the profiles of hearing loss and to find possible relations between profiles of hearing loss and the location of the perforation on the TM.ResultsCluster analysis revealed three types of hearing loss. The mean hearing loss in cluster 1 (6 cases) was above 30 dB, mainly as the result of perforation after chronic otitis media. Hearing loss in clusters 2 (9 cases) and 3 (12 cases) was less than 30 dB. In cluster 2 the perforation was mostly located in the posterior quadrants, while in cluster 3 it was most commonly in the inferior quadrants. In clusters 2 and 3, perforation was usually caused by slap of the open hand, injury, or past ventilation tube.ConclusionsThree different profiles (clusters) of hearing loss resulting from TMP were identified. Force of injury, etiology of the injury, and inflammation produce different sizes of perforations. Conductive hearing loss increases with perforation size and is independent of TM location. In general, hearing loss classification methods have the potential to improve diagnostic procedures, surgery, and rehabilitation of patients with TMPs.

Hereditary hearing loss

Understanding the genetic basis of hearing loss is becoming increasingly relevant, as 50-70% of congenital hearing loss is hereditary and postlingual hearing loss is also often of hereditary origin. To date, more than 220genes for hearing loss have been identified and more than 600syndromes with hearing loss described. This review article explains the classification of genetic hearing loss into syndromic versus non-syndromic forms and the modes of inheritance involved. Some of the most common syndromes (Usher, Pendred, Jervell-Lange-Nielsen, Waardenburg, branchiootorenal, and Alport syndrome) are introductorily described. New sequencing technologies have significantly expanded the diagnostic options for genetic hearing loss and made them more accessible. This text aims to encourage initiation of genetic diagnosis in hearing-impaired patients with suspected hereditary genesis in order to provide the best possible counseling for affected individuals and their families.

Correlation of hearing outcome in otic capsule sparing temporal bone fractures using temporal bone sub-site classification: a cross-sectional descriptive study

BackgroundHead injury most commonly occur following road traffic accidents. Often hearing loss and other otological injuries are missed in view of serious head injury or other visible bodily injuries, leading to delayed recognition of problems. There is a need for early clinical and radiological indicators which can predict adverse outcome. Early diagnosis of otological injury during initial evaluation of the head injury has given some clues regarding the severity of hearing outcomes and such correlation has been attempted in this study. Two-year cross-sectional study involving head injury patients with radiological evidence of temporal bone fracture were assessed for hearing impairment.ResultsThirty-seven patients who satisfied the inclusion criteria were assessed. Fifty-one percent patients had conductive hearing loss. 35.14% of the patients had involvement of mastoid, external auditory canal, and middle ear involvement. A weakly positive correlation was noted between otic capsule sparing fracture and hearing loss. (R value + 0.2064).ConclusionWeak correlation was observed between temporal bone sub-site classification and severity of hearing loss in otic capsule sparing temporal bone fractures.

DESIGN AND IMPLEMENTATION OF A PORTABLE AUTOMATED AUDIOMETER FOR HEARING CLASSIFICATION USING MACHINE LEARNING APPROACHES

Audiometric tests can identify the hearing loss at specific frequencies using the audiogram. The aim and objectives of the study were (i) to develop an automated audiometer for self-diagnosing the hearing ability of the patient; (ii) to extract the features from the acoustic signals and to classify the normal and profound hearing loss patients using different machine learning algorithms; (iii) to validate the hearing loss classification using six-frequency average (6-FA) method based on simple linear regression analysis and machine learning algorithms. The study is conducted among 150 patients, including 75 patients with normal hearing ability and 75 patients with profound hearing loss. The total population of 150 underwent audiometric test both in the soundproof audiometric room and in the normal field environment. Based on the patient response, the intensity and frequency are changed automatically, and the audiogram is plotted by the principle of Artificial Neural Network learning procedures. The overall accuracy produced by classification of normal and profound hearing loss patients using Support Vector Machine (SVM), k-Nearest Neighbor classifier, and Naïve Bayes classifier is 97%, 96%, and 95%, respectively. The results indicated that the SVM classifier outperforms the other two classifiers well. The preliminary audiometric test can be performed remotely and then consulted with an audiologist. Thus, the patient could operate the developed prototype independently and get a consultation from trained medical personnel.

Hearing Loss in Patients With Morquio Syndrome: Protocol for a Scoping Review.

BackgroundMild to moderate hearing loss is common in patients with mucopolysaccharidosis (MPS) IVA. The hearing loss can be conductive, sensorineural, or mixed. However, in these patients, the mixed form is frequent, attributed to the combination of conductive and neurosensory elements, with slowly progressive evolution. Conductive hearing loss may be secondary to recurrent upper respiratory tract infections, serous otitis media, and deformities of the ear ossicles due to the accumulation of glycosaminoglycans (GAGs). Meanwhile, the sensorineural form is mainly attributed to the accumulation of GAGs in the auditory system.ObjectiveThe aim of this scoping review is to understand the extent and type of evidence in relation to the physiopathology, classification, epidemiology, and clinical management of hearing loss and the effect of therapy for hearing loss in patients with MPS IVA.MethodsThis scoping review includes participants across all genders and of no particular age group who are diagnosed with MPS IVA and develop hearing loss as a comorbidity. No exclusion criteria (country, language, or document type) will be applicable. The information sources will include experimental and quasi-experimental, analytical observational, observational, and qualitative studies. Unpublished literature will not be covered. Grey literature will be covered. A total of 2 independent reviewers will participate in the process of screening the literature, paper selection, and data extraction, and this process will be performed blindly. When all manuscripts have been selected, disagreements that arise between the 2 reviewers at each stage of the selection process will be resolved through discussion or with an additional reviewer. Results will be reported with descriptive statistics and information will be displayed in a diagrammatic or tabular manner, as explained in the JBI guidelines.ResultsThe literature search was performed in November 2021 in MEDLINE, LILACS (Literatura Latino-Americana e do Caribe em Ciências da Saúde), the Cochrane Library, ScienceDirect, Google Scholar, and OpenGrey; a total of 780 results were retrieved. Completion of the review is expected in mid-2022.ConclusionsThis scoping review will be the first to describe the extent of the information regarding the development of hearing loss in the MPS IVA population. The data gathered by this review may lead to an understanding of the grade of hearing loss in this population and allow for the assessment of possible interventions according to the disease pattern.International Registered Report Identifier (IRRID)PRR1-10.2196/32986

Speech Perception in Bilateral Hearing Aid Users With Different Grades of Asymmetric Hearing Loss.

Hearing loss is associated with decreased speech perception as well as with changes in the auditory pathway. The effects of those changes on binaural speech perception with hearing aids are not yet fully understood. To provide further evidence on the functional changes of the auditory pathway, several speech perception tests (unilateral and bilateral, aided and unaided, in quiet, and in noise) were conducted in a population of 370 bilateral hearing aid users covering the entire range of the World Health Organization’s most recent classification of hearing loss. To characterize the effects of asymmetric hearing thresholds, a generalized linear model was used for regression analysis. The model revealed a detrimental effect of the poorer ears’ thresholds on both the unaided and the aided unilateral word recognition scores that were attained by the better ear. Moreover, aided binaural word recognition (in quiet and in noise) was affected to a degree that cannot be explained on the sole basis of bilateral summation. Thus, this study provides evidence that there is reorganization and altered functioning of the afferent and efferent auditory pathways due to asymmetric hearing loss. Consequently, more attention should be paid to provision with a hearing aid as early as possible, and separately for each ear.

WULIRA APP mobile phone application to assess occupational hearing loss among industrial workers in Kampala, Uganda

BACKGROUND AND AIM: Occupational hearing loss (OHL) is among the most common causes of disabling hearing loss. Pure Tone Audiometry (PTA) is not readily available in Uganda due to the high cost and operator dependence. However, a mobile phone-health innovation, Wulira App, with a sensitivity of 91.4% in the right ear and 88.4% in the left ear and a specificity of 93.2% and 91.5% in the right and left ear respectively, provides an easy to use, and reliable alternative screening tool for hearing loss. In this study, the prevalence and associated factors of OHL among industrial workers will be determined. METHODS: We will conduct a cross-sectional study among industrial workers in Kampala. A total of 354 participants will be recruited systematically and screened for hearing loss using the Wulira App in a quiet room. Noise cancelling earphones will be used to minimize interference from any ambient noises. The study population will be described by exposure variables such as duration of work in the factory, age, sex, smoking, previous MDR-TB treatment. Categorical data will be presented as frequencies and percentages while continuous variables will be presented as means and standard deviations or medians and interquartile ranges. Proportions for categorical variables will be compared using the chi-square tests and Fisher’s exact tests after testing for assumptions whereas means of continuous variables will be compared using Student t-test for normally distributed data and Mann Whitney U test for skewed data. Inferential statistics will be used. RESULTS:Using a hearing threshold more than or equal to 26 dB as hearing loss, participants with OHL will be presented as proportions and classified according to the WHO classification of hearing loss. CONCLUSIONS:Wulira App is easy to use tool for assessing OHL. Therefore, Wulira App could be adopted in clinical and work settings to screen and monitor for hearing loss. KEYWORDS: Occupational exposure, Occupational epidemiology

Hearing loss classification via AlexNet and extreme learning machine

Hearing is a very important feeling for humans. It helps to perceive the environment around us and can warn us of any imminent dangers around us. Hearing loss is an impaired ability to hear sound. Sensorineural hearing loss (SNHL) is very common in today's society. Classification of hearing loss is helpful for clinical diagnosis, finding suitable treatment methods and allocating appropriate medical interventions. This paper presents a novel method for hearing loss classification based on magnetic resonance images, which can automatically recognize tissue specific features in a given MRI image. The study involved magnetic resonance images (MRI) of 60 participants in three categories of balance: left SNHL, right SNHL, and a healthy control group. Our method uses AlexNet to extract the features of a single category, and uses Extreme Learning Machine to form a triclassifier for automatic SNHL classification. From the experimental results and the practicability of the algorithm, the classification performance is obviously better than the standard deep learning method and other traditional methods.

Measurement Properties of the Hearing Environments and Reflection of Quality of Life (HEAR-QL) 28-item Questionnaire in Cholesteatoma

We sought to assess the reliability and construct validity of the Hearing Environments and Reflection on Quality of Life Adolescent (HEAR-QL 28) quality of life measure (QoL) in cholesteatoma. Observational. Tertiary referral center. One hundred seventeen patients with a diagnosis or history of cholesteatoma completed HEAR-QL 28. In addition to patients within the age range recommended for HEAR-QL 28 (13-18 yr), patients under 13 years old who were able to complete HEAR-QL 28 without parental assistance were included. Completion of HEAR-QL 28 QoL measure. HEAR-QL 28 score, four tone average pure tone audiogram hearing threshold and categorical classification of hearing loss as mild, moderate, and severe. HEAR-QL 28 did not demonstrate discriminative ability on the basis of audiometric threshold, but did discriminate between participants hearing normally (four tone average pure tone audiogram <30 dB HL) (HEAR-QL 86/100) after cholesteatoma surgery from those with unilateral hearing loss (HEAR-QL 73/100) (p < 0.001). Those with unilateral loss could in turn be differentiated from those with bilateral loss (HEAR-QL 60/100) (p < 0.006). HEAR-QL 28 is valid measure of QoL in cholesteatoma with no evidence of redundancy and excellent internal consistency. The importance of considering QoL impact of cholesteatoma is highlighted by 17% of participants reporting the normality or abnormality of their hearing differently from their audiometric threshold. The HEAR-QL 28 provides insight into the ability to cope with their hearing environment in a specific environment.

Sensorineural hearing loss classification via deep-HLNet and few-shot learning

We propose a new method for hearing loss classification from magnetic resonance image (MRI), which can automatically detect tissue-specific features in a given MRI. Sensorineural hearing loss (SHNL) is highly prevalent in our society. Early diagnosis and intervention have a profound impact on patient outcomes. A solution to provide early diagnosis is the use of automated diagnostic systems. In this study, we propose a novel Deep-HLNet framework, based on few-shot learning, for the automated classification of SNHL. This research involves magnetic resonance (MRI) images from 60 participants of three balanced categories: left-sided SNHL, right-sided SNHL, and healthy controls. A convolutional neural network was employed for feature extraction from individual categories, while a neural network and a comparison classifier strategy constituted a tri-classifier for SNHL classification. In terms of experiment results and practicability of the algorithm, the classification performance was significantly better than the standard deep learning methods or other conventional methods, with an overall accuracy of 96.62%.

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Introduction: Sensorineural hearing loss is associated with many complications and needs timely detection and diagnosis. Objectives: Optimize the sensorineural hearing loss detection system to improve the accuracies of image detection. Method: The stationary wavelet entropy was used to extra

Establishment of significant improvement of hearing post-surgery for mucosal type of chronic suppurative otitis media

&lt;p&gt;Chronic suppurative otitis media occurs in the population as a common disease among people living in low socio-economic status. The associated complaints such as hearing difficulty, irritation, discharge makes it difficult for the people to carry out their daily activities. Therefore, their immediate need for treatment occurs. This review aims at analysing the post-operative outcome of hearing following tympanoplasty in order to overcome their morbidity. The source of this study has been obtained from Pubmed, Google scholar and EMBASE. The material for this review was selected from a reference published between January 1975 to December 2014, with this analysis was made. The pre-op ABG in the studies were all falling in mild to moderate hearing loss according to WHO classification of hearing loss. The patients were grouped in Austin-Kartush criteria, groups A to E. The Austin-Kartush classification group A had 3 studies included. The mean ABG for each were 11.5,8.3 and 13.5. In group B,3studies were included. The mean ABG for each were 11.9,10.9 and 11.1. Group C had 4 studies. The mean ABG were 17.7,15,17.1 and 15.7. Group D had 4 studies included which had the same mean ABG 16.6. Group E had just 1 study of mean ABG 20.1. This review helped to establish that there was significant improvement in hearing post-surgery, in patients with varying degrees of hearing loss.&lt;/p&gt;