Hearing aid malfunction detection system

A malfunction detection system for detecting malfunctions in electrical signal processing circuits is disclosed. Malfunctions of a hearing aid in the form of frequency distortion and/or inadequate amplification by the hearing aid amplifier, as well as weakening of the hearing aid power supply are detectable. A test signal is generated and a timed switching circuit periodically applies the test signal to the input of the hearing aid amplifier in place of the input signal from the microphone. The resulting amplifier output is compared with the input test signal used as a reference signal. The hearing aid battery voltage is also periodically compared to a reference voltage. Deviations from the references beyond preset limits cause a warning system to operate

Decision support system for the selection of an ITE or a BTE hearing aid

The purpose of this research is to mine a large set of heterogeneous audiology data to create a decision support system (DSS) to choose between two hearing aid types (ITE and BTE aid). This research is based on the data analysis of audiology data using various statistical and data mining techniques. It uses the data of a large NHS (National Health Services, UK) facility. It uses 180,000 records (covering more than 23,000 different patients) from a hearing aid clinic. The developed system uses an unconventional method to predict hearing aid type for a patient and it can be used as a second opinion by audiologists for complex cases. After modifying the system to take account of the feedback from a professional audiologist, the success rates obtained were in the ranges 63 to 66 percent. In this research an automatic system was developed to choose between an ITE or a BTE hearing aid type with an explanation facility that can be used as a second opinion by audiologist in cases where the choice of an ITE or a BTE hearing aid is not clear cut. This analysis of audiology data and DSS will provide supplementary information for audiology experts and hearing aid dispensers. This type of system may also be of interest to manufacturers of hearing technologies in using as a ready means for their telephone customer services staff to check data, discovering data in audiology records will also be good for general awareness about the suitability of hearing aid type

Hearing aid controlled by binaural source localizer

An adaptive directional hearing aid system comprising a left hearing aid and a right hearing aid, wherein a binaural acoustic source localizer is located in the left hearing aid or in the right hearing aid or in a separate body- worn device connected wirelessly to the left hearing aid and the right hearing aid, the binaural acoustic source localizer configured to receive input signals from the left hearing aid and the right hearing aid and generate a control signal to control the update of a first adaptive beam former in the left hearing aid and a second adaptive beam former in the second hearing aid is disclosed. The disclosed system improves speech intelligibility and listening comfort for the user in noisy environments

Level discrimination of speech sounds by hearing-impaired individuals with and without hearing amplification

Objectives: The current study was designed to see how hearing-impaired individuals judge level differences between speech sounds with and without hearing amplification. It was hypothesized that hearing aid compression should adversely affect the user's ability to judge level differences. Design: Thirty-eight hearing-impaired participants performed an adaptive tracking procedure to determine their level-discrimination thresholds for different word and sentence tokens, as well as speech-spectrum noise, with and without their hearing aids. Eight normal-hearing participants performed the same task for comparison. Results: Level discrimination for different word and sentence tokens was more difficult than the discrimination of stationary noises. Word level discrimination was significantly more difficult than sentence level discrimination. There were no significant differences, however, between mean performance with and without hearing aids and no correlations between performance and various hearing aid measurements. Conclusions: There is a clear difficulty in judging the level differences between words or sentences relative to differences between broadband noises, but this difficulty was found for both hearing-impaired and normal-hearing individuals and had no relation to hearing aid compression measures. The lack of a clear adverse effect of hearing aid compression on level discrimination is suggested to be due to the low effective compression ratios of currently fit hearing aids

Targeted Re-Instruction for Hearing Aid Use and Care Skills

Purpose: Approximately 30% of hearing aid owners do not wear their hearing aids. One of the main reasons reported for hearing aid non-use is that hearing aid owners cannot successfully use and/or care for their hearing aids (Lupsakko, Kautiainen, & Sulkava 2005; Popelka et al. 1998; Vuorialho, Karinen, & Sorri 2006). The primary purpose of the present study was to evaluate the benefit of identifying specific hearing aid use and care skills that a hearing aid user cannot perform or has difficulty performing and providing re-instruction on those specific skills. This is operationally defined in the present study as targeted re-instruction. A second purpose was to determine if adding targeted re-instruction to a hearing aid fitting would result in greater hearing aid satisfaction and more hours of daily hearing aid use. Last, factors that may influence an individual’s learning and remembering of hearing aid use and care skills were also assessed. Method: Twenty-six participants (13 experimental; 13 control) were included in this randomized control trial. All participants were new hearing aid users who had never worn or tried hearing aids before. Participants were fit with ReSound Linx 3D 962 RIC-style hearing aids for a four-week trial period and provided a standard hearing aid orientation. Participants assigned to the experimental group were also administered the Practical Hearing Aid Skills Test – Revised (PHAST-R; Desjardins & Doherty 2009; Doherty & Desjardins 2012) and provided targeted re-instruction. Hearing aid use and care skills were measured using the Hearing Aid Skills and Knowledge (HASK; Saunders et al. 2018) test immediately following the hearing aid fitting and then again at four weeks post-hearing aid fitting. The relationship between hearing aid use and care skills and measures of hearing handicap, hearing aid-related attitudes, and working memory were assessed. In addition, hearing aid satisfaction was measured at two and four weeks post-hearing aid fitting. Data logging was used to determine average daily hours of hearing aid use. Results: Participants in the experimental group maintained their hearing aid use and care skills over the four week hearing aid trial, but participants in the control group showed a significant decline in their hearing aid use and care skills over the same time period. None of the factors assessed in the present study were significantly correlated to learning and remembering of hearing aid use and care skills. Also, no significant difference in average daily hours of hearing aid use was observed between the two groups of participants. Level of satisfaction was significantly different between the two groups at two weeks post-hearing aid fitting, but not at the end of the four week trial period. Last, it took an average of 9 minutes and 15 seconds (SD = 3 minutes and 13 seconds) to administer the PHAST-R app and provide targeted re-instruction. Conclusions: Targeted re-instruction prevented a decline in hearing aid use and care skills after four weeks of hearing aid use. Participants who did not receive targeted re-instruction showed a decline in their hearing aid use and care skills after only four weeks of hearing aid use. This indicates that targeted re-instruction can help new hearing aid users maintain their hearing aid use and care skills over time. It took an average of less than 10 minutes to administer the PHAST-R app and provide targeted re-instruction

Deep Denoising for Hearing Aid Applications

Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.Comment: submitted to IWAENC 201

Decision support system to help choose between an ITE or a BTE hearing aid

A decision support system (DSS) is used for analysing a situation and making decisions. The goal of this research is to mine a large set of heterogeneous audiology data and create a DSS to help audiology technicians to choose between an ITE or BTE hearing aid. Although, in many cases such a choice is clear cut, but at other times this system could be used as a second opinion to predict the hearing aid type. A number of data mining techniques, such as clustering of audiograms, association analysis of variables (such as, age, gender, diagnosis, masker, mould and free text keywords) using contingency tables and principal component analysis on audiograms were used to find candidate variables to be combined into a DSS. The DSS was created using the techniques of logistic regression, Naïve Bayesian analysis and Bayesian networks, and these systems were tested and validated on test data to see which of the techniques produced the better results. This DSS takes air and bone conduction frequencies, age, gender, diagnosis, masker, mould and some free text words associated with a patient as input and gives as the output a decision as to whether the patient would be more likely to prefer an ITE or a BTE hearing aid type. The highest agreement between predicted results and actual hearing aid type in the data were obtained using Bayesian networks, with 93 to 94 percent similarity overall, with a precision of 0.91 for ITE and 0.96 for BTE. The reason for this might be that the Bayesian network also considers interaction between variables while the other two techniques (logistic regression and Naïve Bayesian analysis) consider only the individual variables. One of the important features of this DSS is that once the final choice of hearing aid type is predicted, the decision process can be tracked back to see which factors (variables) contributed how much to the final decision. The theoretical upper bound of classifier performance is the inter-annotator agreement (Altman, 1991), in this case the rate at which two expert audiologists would assign the same hearing aid to the same patient. Unfortunately, this type of data was not included in the audiology database

Direction discriminating hearing aid system

A visual display was developed for people with substantial hearing loss in either one or both ears. The system consists of three discreet units; an eyeglass assembly for the visual display of the origin or direction of sounds; a stationary general purpose noise alarm; and a noise seeker wand

Bond graph based sensitivity and uncertainty analysis modelling for micro-scale multiphysics robust engineering design

Components within micro-scale engineering systems are often at the limits of commercial miniaturization and this can cause unexpected behavior and variation in performance. As such, modelling and analysis of system robustness plays an important role in product development. Here schematic bond graphs are used as a front end in a sensitivity analysis based strategy for modelling robustness in multiphysics micro-scale engineering systems. As an example, the analysis is applied to a behind-the-ear (BTE) hearing aid. By using bond graphs to model power flow through components within different physical domains of the hearing aid, a set of differential equations to describe the system dynamics is collated. Based on these equations, sensitivity analysis calculations are used to approximately model the nature and the sources of output uncertainty during system operation. These calculations represent a robustness evaluation of the current hearing aid design and offer a means of identifying potential for improved designs of multiphysics systems by way of key parameter identification

Comparison of hearing aid outcome measures in adult hearing aid users

Hearing aid outcome measures have become an essential part of audiological intervention. This study aimed to explore hearing aid benefit in Maltese hearing aid users through subjective and objective outcome measures. The Profil Imqassar dwar il-Benefiċċju tal-Hearing Aids (PIBHA), a translated version of the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire, was used to subjectively examine hearing aid benefit in 56 adult hearing aid users falling in the 20- to 60-year age range. Thirty of these hearing aid users subsequently participated in clinical testing to evaluate hearing aid benefit objectively. Real ear measurements (REMs) and two non-word repetition tests, the Maltese Non-Words in Quiet (MNWQ) and the Maltese Non-Words in Noise (MNWN), were used in the study. Analysis aimed to identify which factors correlated with hearing aid benefit. It also explored the extent to which subjective perception of hearing aid benefit correlated with performance on non-word repetition and REMs in the local population. Daily use was associated with gender and hearing aid type. Non-word repetition scores were correlated with the PIBHA scores and with REMs. Unlike findings reported in the literature, REMs were not correlated with the self-reported measures of the PIBHA. Implications for including both subjective and objective measures in hearing aid fitting protocols are addressedpeer-reviewe