Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)

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Biosignal Feature Extraction with Deep Learning: Applications in Clinical Diagnostics and Therapeutics

Authors
Feng Liu1, Yichen Yu2, Yuxi Yao3, *
1Suzhou Foreign Language School, Suzhou City, Jiangsu Province, 215011, China
2Shenzhen (Nanshan) Concord College of Sino-Canada, Shenzhen, 518052, China
3Beijing University of Technology, Beijing, 100124, China
*Corresponding author. Email: yaoyuxicindy@gmail.com
Corresponding Author
Yuxi Yao
Available Online 18 February 2026.
DOI
10.2991/978-94-6463-986-5_62How to use a DOI?
Keywords
Deep Learning; Bio signal Processing; Disease Recognition; Feature Extraction; 5G/Io
Abstract

Biological signals (such as electrocardiogram and electroencephalogram) hold significant value in disease diagnosis and treatment. However, traditional methods of processing these signals suffer from low efficiency and vulnerability to noise interference. With the integrated development of AI and communication technologies, such as 5G and the Internet of Things, a new path has been opened for precision medicine. This study focuses on exploring how AI can leverage the extraction of biological signal features and communication optimisation to assist in disease treatment. By conducting a literature review to summarise the latest theories and analysing successful treatment cases, the research results show that AI can significantly improve the accuracy of biological signal feature extraction, and communication technologies can effectively ensure real-time treatment responses. Moreover, the fusion of multi-modal signals and lightweight AI will become a key direction for future development in this field, providing support for the advancement of disease identification and treatment towards greater precision and efficiency.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
Series
Advances in Engineering Research
Publication Date
18 February 2026
ISBN
978-94-6463-986-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-986-5_62How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Feng Liu
AU  - Yichen Yu
AU  - Yuxi Yao
PY  - 2026
DA  - 2026/02/18
TI  - Biosignal Feature Extraction with Deep Learning: Applications in Clinical Diagnostics and Therapeutics
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 605
EP  - 614
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_62
DO  - 10.2991/978-94-6463-986-5_62
ID  - Liu2026
ER  -