An Optimized Hybrid Approach to Denoising of EEG Signals Using CNN and LMS Filtering
Uloženo v:
| Vydáno v: | Electronics vol. 14, no. 6 (2025), p. 1193 |
|---|---|
| Hlavní autor: | |
| Další autoři: | , |
| Vydáno: |
MDPI AG
|
| Témata: | |
| On-line přístup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tagy: |
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3181457743 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14061193 |2 doi | |
| 035 | |a 3181457743 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Nair, Suma |u Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India; <email>brittopariece@veltech.edu.in</email> | |
| 245 | 1 | |a An Optimized Hybrid Approach to Denoising of EEG Signals Using CNN and LMS Filtering | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Sleep is a physiological signal which plays a vital role in maintaining human health and well-being. Polysomnographic records provide insights into the various changes occurring during sleep, and hence its study is important in diagnosing various disorders including sleep disorders. As polysomnographic records encapsulate several biological signals, an extraction of EEG signals requires efficient denoising. Thus, a reliable tool for artifact removal is essential in the field of biomedical applications. The CNN is used for its feature extraction and robustness and the least mean square filter for its noise suppression. As the techniques complement one another, a combination of both leads to a better denoised EEG signal. In this approach, CNN is used for the precise removal of artifacts and then an LMS filter is used for its effective adaptation in real-time. The hybridization of both techniques in a hardware-based environment is largely. unexplored. As a result, this study proposes an integration of convolutional neural networks and least mean square filtering for an efficient denoising of EEG signals. Both techniques are optimized to tailor the design to hardware requirements. CNN is refined using the Strassen–Winograd algorithm. The Strassen–Winograd algorithm simplifies matrix multiplication, contributing to a more hardware-optimized design. In this study LMS filtering is analyzed and optimized using several optimizations. The optimizations are two’s complement distributed arithmetic algorithm, offset binary coding-based distributed arithmetic, offset binary coding Radix 4-based distributed arithmetic, as well as a Coordinate Rotation Digital Computer. The CNN with offset binary radix 4 distributed arithmetic-based LMS filter has resulted in a decrease in area of 77% and a decrease in power by 69.1%. But, in terms of Signal to Noise Ratio, Mean Squared Error and Correlation Coefficient, the CNN with offset binary coding distributed arithmetic-based LMS filter has shown better performance. The design was synthesized and implemented in Vivado 19.1. The power and area reduction in this study makes it even more suitable for wearable devices. | |
| 653 | |a Physiology | ||
| 653 | |a Artifacts | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Arithmetic | ||
| 653 | |a Hardware | ||
| 653 | |a Binary codes | ||
| 653 | |a Biomedical materials | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Noise reduction | ||
| 653 | |a Neural networks | ||
| 653 | |a Multiplication & division | ||
| 653 | |a Wearable technology | ||
| 653 | |a Multiplication | ||
| 653 | |a Algorithms | ||
| 653 | |a Electroencephalography | ||
| 653 | |a Arithmetic coding | ||
| 653 | |a Digital computers | ||
| 653 | |a Real time | ||
| 653 | |a Filtration | ||
| 653 | |a Correlation coefficients | ||
| 653 | |a Signal to noise ratio | ||
| 700 | 1 | |a Britto, Pari James |u Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India; <email>brittopariece@veltech.edu.in</email> | |
| 700 | 1 | |a Man-Fai Leung |u School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB11PT, UK; <email>man-fai.leung@aru.ac.uk</email> | |
| 773 | 0 | |t Electronics |g vol. 14, no. 6 (2025), p. 1193 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181457743/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181457743/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181457743/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |