Modelling Audiograms for People with Dementia Who Experience Hearing Loss Using Multiple Linear Regression Method

Elkhouly, Abeer; Rahim, Hasliza A.; Abdulaziz, Nidhal; Malek, Mohamed Fareq Abd

doi:10.1109/ccci49893.2020.9256679

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Contemporary hearing aids employ dynamic multiband compression schemes, which separate the input signal into multiple bands [5]. This multiband approach was developed to address the limitations inherent in single-band compression systems [6]. Nevertheless, such a multiband compression module often results in reduced modulation and spectral efficiency in communication signals [7].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

Shrivastav,

Bhandari,

Kolte

2024

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Hearing aid systems rely on reconfigurable filters to selectively amplify desired signals while suppressing unwanted noise. However, the complexity of the hardware and the associated power dissipation present substantial challenges, particularly in systems where adaptability and scalability are crucial. These systems require advanced auditory compensation to accommodate the diverse auditory profiles of individuals with hearing impairments. In this study, an African buffalo-inspired optimization algorithm is introduced to fine-tune a Frequency Response Masking Reconfigurable Filter (AB-FRMRF). This novel approach ensures High-Q filtering with narrow transition bands while maintaining a low filter order, thereby enhancing selectivity and minimizing complexity. The optimization algorithm adjusts its search strategy based on solution fitness, optimizing the filter's coefficient values. Performance parameters such as matching error and group delay are meticulously tuned for optimal auditory compensation. Utilizing distributed arithmetic for hardware realization and a Brent Kung adder for the summation of dynamic random-access memory (DRAM) partial products, the hardware design deviates from conventional pipeline adder trees, culminating in significant power savings and improved processing speed. Modeled in MATLAB and implemented on a Kintex 7 FPGA Genesys 2 board, the AB-FRMRF model exhibits a reduced matching error down to 1.2 dB and a minimized delay to 2.5 msec for high-frequency sounds in cases of mild hearing loss. These metrics are favorable in comparison to existing reconfigurable filters reported in the literature. The AB-FRMRF model not only demonstrates a 35% improvement in power consumption but also outperforms traditional methods in computational complexity, auditory compensation, delay, and power metrics, making it highly suitable for hearing aid applications. The proposed filter design signifies a substantial advancement in auditory assistance technology, aligning with the necessity for high-performance, low-power hearing aid devices.

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Section: Introductionmentioning

confidence: 99%

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

Shrivastav,

Bhandari,

Kolte

2024

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“…Generally, the degree of hearing loss of patients is represented by an audiogram. An audiogram shows a person's ability to hear at different frequencies and preserves valuable information related to the underlying causes of hearing impairment [14,24]. Therefore, it is a technical means worth investigating to classify patients according to audiograms to apply fitting-free hearing aids.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Liang

Wang

et al. 2023

Multimed Tools Appl

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Hearing 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.

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An optimized frequency response masking reconfigurable filter to enhance the performance of the hearing aid system

Shrivastav,

Kolte

2024

Multimed Tools Appl

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Modelling Audiograms for People with Dementia Who Experience Hearing Loss Using Multiple Linear Regression Method

Cited by 5 publications

References 14 publications

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

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

An optimized frequency response masking reconfigurable filter to enhance the performance of the hearing aid system

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