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Artificial Intelligence-Based Hearing Loss Detection Using Acoustic Threshold and Speech Perception Level

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Abstract

Hearing loss detection using automated audiometers and artificial intelligence methods has gained increasing attention in recent years. The proposed work aims: (a) to design an automated audiometer to diagnose hearing ability and to evaluate hearing intensity for healthy and profound hearing loss patients within 250 Hz to 8 kHz, (b) to compare the proposed automated audiometer with a conventional audiometer when estimating auditory perception level using pure tone and speech audiometers, and (c) to use the machine learning algorithms to classify hearing loss and normal subjects based on the selected features extracted from speech signals. Participants in the study included 50 healthy individuals and 50 patients with profound hearing loss. In the proposed hardware unit, the transmitted pure-tone signal and the speech signal stimulus are controlled automatically instead of being controlled manually. Using a digital potentiometer, a pure-tone audiometer can be automatically calibrated by varying the frequency and intensity of the generated tones according to the users’ responses. During speech audiometric measurements, pre-recorded speech and repeated speech signals are analyzed to estimate speech recognition threshold (SRT) and word recognition score (WRS). The designed audiometer plots the audiogram automatically, estimating SRT and WRS, and classifying the subject as normal or hearing impaired. This study demonstrates the feasibility of using Machine Learning to predict hearing impairment in patients. A support vector machine, a random forest, and an AdaBoost model produced accuracy rates of 98%, 96%, and 96%, respectively, when identifying normal and hearing loss subjects. The proposed audiometer system is miniaturized, portable, and user-friendly in comparison to conventional audiometers. Consequently, the prototype would make it possible for subjects to conduct their own audiometric tests independently and send the results along with their audiogram to a trained medical professional to receive advice.

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Acknowledgements

We would like to thank all the participants who took part actively in the audiometer tests. We confirm that all the subjects agreed to participate in the study and gave their informed consent for publication.

Funding

The authors state no funding is involved.

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Authors and Affiliations

  1. Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu, 603203, India

    V. M. Raja Sankari & U. Snekhalatha

  2. Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, 13133, Doha, Kuwait

    M. Murugappan

  3. Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai, Tamil Nadu, India

    M. Murugappan

  4. Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, 02600, Perlis, Malaysia

    M. Murugappan

  5. Department of Electrical Engineering, College of Engineering, Qatar University, 2713, Doha, Qatar

    Muhammad E. H. Chowdhury

  6. Surgical Department, Amiri Hospital, Sharq, Kuwait

    Zeinab A. Chamkha

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  1. V. M. Raja Sankari
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  2. U. Snekhalatha
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  3. M. Murugappan
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  4. Muhammad E. H. Chowdhury
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  5. Zeinab A. Chamkha
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Corresponding authors

Correspondence to U. Snekhalatha or M. Murugappan.

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All the authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee. The study was approved by the Bioethics Committee of SRM Research Centre and Hospital with Ethics Clearance Number 1769/IEC/2019.

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Fig. 8
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Software implementation of the decision-making module using labview

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Sankari, V.M.R., Snekhalatha, U., Murugappan, M. et al. Artificial Intelligence-Based Hearing Loss Detection Using Acoustic Threshold and Speech Perception Level. Arab J Sci Eng 48, 14883–14899 (2023). https://doi.org/10.1007/s13369-023-07927-1

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  • DOI: https://doi.org/10.1007/s13369-023-07927-1

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