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Predicting cochlear implants score with the aid of reconfigured long short-term memory

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Abstract

A surgical procedure namely the Cochlear implantation aims in fitting the electronic device the cochlear implant. This electronic device helps person with moderate to severe hearing loss. It becomes very important to treat children with auditory deprivation much earlier, since it prohibits their language development skill too. This research aims to develop a model that can be used to guide Cochlear Implants (CI) programming for new patients in the children of 5 to 10 ages using visual cross modal data obtained from previously programmed patients. The cohort chosen is bilateral congenitally deaf children. This age group is selected since their language development is affected due to their auditory deprivations. The design is based on obtaining the analysis of cross modal plasticity using the visual evoked potential. AI based techniques, which is formed using the patient database. The goal is to use patients, real time database collected from the children and observe if it is likely to discover patterns in the data that can predict something about future patients. The resolution would be a program that can discover factors for the auditory deprived. The objective of this work is to apply Long Short-Term Memory (LSTM) network based Artificial Intelligence (AI) model to discover the unknown pattern. LSTM is suited to classify, process and predict time series given time lags of unknown duration. Relative insensitivity to gap length gives an advantage to LSTM over alternative RNNs. To augment an additional performance, the investigation comprises Enhanced Swarm based Crow Search Optimization (ESCSO) to identify optimal weights. The results exhibit the dominance of suggested ESCSO based LSTM technique over other techniques.

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Authors and Affiliations

  1. Biomedical Engineering, Jerusalem College of Engineering, Chennai, India

    M. S. Jeyalakshmi

  2. Computer Science and Engineering, Sri Sairam Engineering College, Chennai, India

    C. R. Rene Robin

  3. Computational Intelligence Research Foundation, Chennai, India

    D. Doreen

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  1. M. S. Jeyalakshmi
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  2. C. R. Rene Robin
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  3. D. Doreen
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Correspondence to M. S. Jeyalakshmi.

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Jeyalakshmi, M.S., Robin, C.R.R. & Doreen, D. Predicting cochlear implants score with the aid of reconfigured long short-term memory. Multimed Tools Appl 82, 12537–12556 (2023). https://doi.org/10.1007/s11042-022-13812-0

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  • DOI: https://doi.org/10.1007/s11042-022-13812-0

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