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Sensorineural Hearing Loss Identification via Discrete Wavelet Packet Entropy and Cat Swarm Optimization

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Applied Nature-Inspired Computing: Algorithms and Case Studies

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Abstract

(Aim) Currently, there are many methods to identify sensorineural hearing loss via magnetic resonance imaging. This study aims to develop a more efficient approach. (Methods) Our approach used discrete wavelet packet entropy as the feature-extraction method. It used single-hidden layer feedforward neural network as the classifier model. A bio-inspired algorithm, cat swarm optimization (CSO) is employed to train the weights/biases of this neural network. (Results) Simulation results showed our approach achieved an overall accuracy of 92.33%. Besides, this cat swarm optimization method gives better performance than genetic algorithm, particle swarm optimization, firefly algorithm, chaotic simulated annealing, and adaptive genetic algorithm. The whole hearing loss identification system yields greater accuracy than three state-of-the-art methods. It costs 102.8 ms to predict a new brain image. (Conclusion) The discrete wavelet packet entropy is an efficient feature-extraction method for detecting hearing loss. The CSO algorithm may be applied in other optimization problems.

# Shui-Hua Wang, Ming Yang, Yu-Dong Zhang are contributed equally to this paper.

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Acknowledgements

This paper was supported by the Opening Project of State Key Laboratory of Digital Publishing Technology.

Author information

Authors and Affiliations

  1. State Key Laboratory of Digital Publishing Technology, Beijing, China

    Shui-Hua Wang & Yu-Dong Zhang

  2. School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Shui-Hua Wang

  3. Department of Radiology, Children’s Hospital of Nanjing Medical University, Nanjing, 210008, People’s Republic of China

    Ming Yang

  4. College of Computer Science, Inner Mongolia University, Hohhot, 010012, China

    Shuai Liu

  5. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Yu-Dong Zhang

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  1. Shui-Hua Wang
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  2. Ming Yang
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Corresponding authors

Correspondence to Shuai Liu or Yu-Dong Zhang .

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Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. Research and Development and Academic Affairs, RCC Institute of Information Technology, Kolkata, India

    Siddhartha Bhattacharyya

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Wang, SH., Yang, M., Liu, S., Zhang, YD. (2020). Sensorineural Hearing Loss Identification via Discrete Wavelet Packet Entropy and Cat Swarm Optimization. In: Dey, N., Ashour, A., Bhattacharyya, S. (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_6

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