Development of a Biocompatible Dual-Band Implantable Antenna with Optimized SAR Reduction Techniques for Wireless Transmission of Cardiac and Neural Diagnostics Data
DOI:
https://doi.org/10.31838/NJAP/07.03.24Keywords:
Implantable antenna, SAR optimization, dual-band telemetry, biocompatible encapsulation, neural diagnostics, MICS bandAbstract
Medical implantable devices are strongly dependent on the use of small antennas that have the capability of operating in the dual band and at a stable rate to provide continuous cardiac and neural telemetry. The tissues of humans however present a powerful dielectric loading which leads to impedance detuning, low gain as well as high specific absorption rate (SAR). This is an article that describes a biocompatible dual-band implantable antenna that is designed to achieve stable Medical Implant Communication Service (MICS) and Industry, Scientific, and Medical (ISM) band functionality. A multilayer scheme using a high-permittivity radiator, parylene-C encapsulation and an electromagnetic bandgap (EBG)-based assisted SAR reduction set up is suggested. The visualisation of the system behaviour is represented by figures about 3D structure, layer distribution, S11 response, SAR maps, and radiation patterns. The design is supported by a mathematical model of reliability and an iterative SAR optimization algorithm. Simulations with electromagnetic models of realistic multilayer human tissues show an improved stability of impedance, low SAR, and better gain as compared to reference designs. The antenna facilitates strong wireless communication of the cardiac electrograms and neural activity signals at variable depths of implantation. This contribution further supports the development of the design of biocompatible, safe, and high-performance antennas to be used in long-term implantable diagnostics.
References
1. Aliqab, K., Nadeem, I., & Khan, S.R. (2023). A comprehensive review of in-body biomedical antennas: Design, challenges and applications. Micromachines, 14(7), 1472. https://doi.org/10.3390/mi14071472
2. Amin, B., Rehman, M.R.U., Farooq, M., Elahi, A., Donaghey, K., Wijns, W., Shahzad, A., & Vazquez, P. (2023). Optimizing cardiac wireless implant communication: A feasibility study on selecting the frequency and matching medium. Sensors, 23(7), 3411. https://doi.org/10.3390/s23073411
3. Basir, A., Cho, Y., Shah, I.A., Hayat, S., Ullah, S., Zada, M., Ahson, S., & Yoo, H. (2023). Implantable and ingestible antenna systems: From imagination to realization [bioelectromagnetics]. IEEE Antennas and Propagation Magazine, 65(5), 70–83. https://doi.org/10.48550/arXiv.2306.02434
4. Bianchi, G.F. (2025). Smart sensors for biomedical applications: Design and testing using VLSI technologies. Journal of Integrated VLSI, Embedded and Computing Technologies, 2(1), 53–61. https://doi.org/10.31838/JIVCT/02.01.07
5. Booch, K., Wehrmeister, L.H., & Parizi, P. (2025). Ultralow latency communication in wireless sensor networks: Optimized embedded system design. SCCTS Journal of Embedded Systems Design and Applications, 2(1), 36–42. https://doi.org/10.31838/ESA/02.01.04
6. Chishti, A.R., Aziz, A., Aljaloud, K., Tahir, F.A., Abbasi, Q.H., Khan, Z.U., & Hussain, R. (2023). A sub 1 GHz ultra miniaturized folded dipole patch antenna for biomedical applications. Scientific Reports, 13(1), 9900. https://doi.org/0.1038/s41598-023-36747-4
7. Jasim, M., Al-Gburi, A.J.A., Hanif, M., Dayo, Z.A., Ismail, M.M., & Zakaria, Z. (2025). An extensive review on implantable antennas for biomedical applications: Health considerations, geometries, fabrication techniques, and challenges. Alexandria Engineering Journal, 112, 110–139. https://doi.org/10.1016/j.aej.2024.10.105
8. Madugalla, A.K., & Perera, M. (2024). Innovative uses of medical embedded systems in healthcare. Progress in Electronics and Communication Engineering, 2(1), 48–59. https://doi.org/10.31838/PECE/02.01.05
9. Mohan, A., & Kumar, N. (2024). Implantable antennas for biomedical applications: A systematic review. BioMedical Engineering OnLine, 23(1), 87. https://doi.org/10.1186/ s12938-024-01277-1
10. Agarwal, K., Jegadeesan, R., Guo, Y. X., & Thakor, N. V. (2017). Wireless power transfer strategies for implantable bioelectronics. IEEE reviews in biomedical engineering, 10, 136–161.
11. Reddy, R.N., Rao, N.V.K., Krishna, D.R., & Ghosh, J.(2023). Design of ultra-miniaturized wearable antenna for bio-telemetry applications. Progress in Electromagnetics Research C, 136, 113–121. https://doi.org/10.2528/PIERC23062603
12. Rucker, P., Menick, J., & Brock, A. (2025). Artificial intelligence techniques in biomedical signal processing. Innovative Reviews in Engineering and Science, 3(1), 32–40. https://doi.org/10.31838/INES/03.01.05
13. Sharma, D., Kanaujia, B.K., Kumar, S., Rambabu, K., & Sharma, A. (2023). Low-loss MIMO antenna wireless communication system for 5G cardiac pacemakers. Scientific Reports, 13, 9557. https://doi.org/10.1038/s41598-023-36209-x
14. Gupta, B., Sankaralingam, S., & Dhar, S. (2010, August). Development of wearable and implantable antennas in the last decade: A review. In 2010 10th Mediterranean Microwave Symposium (pp. 251-267). IEEE.
15. Singh, G., & Kaur, J. (2023). Small footprint biocompatible antenna for implantable devices: Design, in-silico, in-vitro and ex-vivo testing. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 47, 1145–1152. https://doi.org/10.1007/s40998-023-00588-8
16. Velliangiri, A. (2024). Security challenges and solutions in IoT-based wireless sensor networks. Journal of Wireless Sensor Networks and IoT, 1(1), 8–14. https://doi.org/10.31838/WSNIOT/01.01.02
17. Moss, D. J. (2025). Thermal investigation of biostability in high index doped silica integrated ring resonators. arXiv preprint arXiv:2502.01642.
18. Jha, P., Sharma, N., & Saxena, D. (2024, November). A Miniaturized Implantable Antenna for Bio-Medical Applications. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1–3). IEEE.
19. Bishnoi, V., & Goel, N. (2023). A color-based deep-learning approach for tissue slide lung cancer classification. Biomedical Signal Processing and Control, 86, 105151.





