Startseite Technik ANN modeling for predicting muscle-implanted antenna performance for skin and fat thickness variations at 2.45 GHz
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ANN modeling for predicting muscle-implanted antenna performance for skin and fat thickness variations at 2.45 GHz

  • Soham Ghosh ORCID logo EMAIL logo und Bhaskar Gupta
Veröffentlicht/Copyright: 21. Oktober 2024
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Abstract

Implantable antenna is embedded in implantable medical devices to communicate with external world for continuous patient monitoring. The implantable antenna performance may vary with patients due to their different skin and fat thickness owing to their environment, location and concentration of collagen in skin, estrogen and testosterone concentration, age, diseases like obesity, Gottron syndrome etc. Estimation of IMA performance requires a proper description of its dependence on variable skin and fat thickness which is not studied extensively in previous related works. In this article, we have designed a muscle-implanted antenna at 2.45 GHz to examine its performance dependence on variable skin and fat thickness values. In this study, we have designed an artificial neural network (ANN) for prediction of antenna performance parameters at 2.45 GHz for wide range of skin and fat thickness variations. The designed ANN model can predict antenna performance with ∼0.99 % error. Here, accurate dependence analysis of radiation performance of implantable antenna in terms of S 11, realized gain, bandwidth and operating frequency on skin and fat thickness variations is performed which has not been studied yet and use of ANN in this field for accurate prediction is also a novel approach.


Corresponding author: Soham Ghosh, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India, E-mail:

Acknowledgments

The authors would like to thank Dr. Kaushik Patra, Dr. Ardhendu Kundu, Dr. Joydeep Pal and Dr. Sayan Sarkar for their support and suggestions throughout this work.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: In previous works, gain of the implanted antenna was primary focus; however, dependence of resonant frequency, bandwidth and S 11 are also important to analyze. We have analyzed all these four parameters in this manuscript. The performance prediction of any muscle implanted antenna due to variable skin and fat thickness values by ANN has not been explored previously to the best of our knowledge. Therefore, it can be concluded that this work has novelty in terms of performance analyses of implantable antenna due to skin and fat thickness variations over previous works. ANN modeling for predicting implantable antenna performance within variable environment is also a novel approach as per our study. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-03-16
Accepted: 2024-09-16
Published Online: 2024-10-21
Published in Print: 2025-01-29

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 31.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/freq-2024-0081/html
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