Optimization of Active Bandpass Filters for Weak Signals Using Quantum Particle Swarm Optimization: A Case Study of Electroencephalogram Signal Power Spectrum
DOI:
https://doi.org/10.62048/qjms.v1i2.46Keywords:
Quantum Particle Swarm Optimization, Optimization, Average Power, Band Pass Filter, ElectroencephalogramAbstract
This research focuses on using Quantum Particle Swarm Optimization to optimize active bandpass filters for weak signals. Electroencephalograph signal data obtained from other researchers was used as a case study in this research. This Electroencephalogram signal data, consisting of 1280 signal amplitudes with a sampling frequency of 256 Hz, was characterized before being fed into Quantum Particle Swarm Optimization for optimization. The optimization goal of Quantum Particle Swarm Optimization is to achieve a signal frequency range with a maximum average power value. For bandpass filter design, the frequency range obtained from Quantum Particle Swarm Optimization is used as a reference. The frequency range of 9.9 Hz to 13 Hz and 15.99 Hz to 30 Hz provides optimal conditions. The filter design is based on the frequency range of optimization results and component values R1, R2, and R3 respectively 5.1M?, 10.2 M?, and 1M? with C1=C2=0.01?F for frequencies 9.9 Hz to 13 Hz, and a value of 1 .2M?, 2.4 M?, and 1M? each with C1=C2=0.01?F for frequencies 15.99 Hz to 30 Hz.
References
Fang, W., Sun, J., Ding, Y., Wu, X., & Xu, W. (2010). A review of quantum-behaved particle swarm
optimization. IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), 27(4), 336–348. https://doi.org/10.4103/0256-4602.64601
Flori, A., Oulhadj, H., & Siarry, P. (2022). Quantum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization. Computational Optimization and Applications, 82(2), 525–559. https://doi.org/10.1007/S10589-022-00362-2/METRICS
Luitel, B., & Venayagamoorthy, G. K. (2010). Particle swarm optimization with quantum infusion for system identification. Engineering Applications of Artificial Intelligence, 23(5), 635–649. https://doi.org/10.1016/J.ENGAPPAI.2010.01.022
Mikki, S. M., & Kishk, A. A. (2006). Quantum particle swarm optimization for electromagnetics. IEEE Transactions on Antennas and Propagation, 54(10), 2764–2775.
https://doi.org/10.1109/TAP.2006.882165
Mundy-Castle, A. C. (1957). The electroencephalogram and mental activity. Electroencephalography and
Clinical Neurophysiology, 9(4), 643–655. https://doi.org/10.1016/0013-4694(57)90085-8
Otniel Windrayadi, F., Rahmatullah, D., Winarno, I., Teknik Elektro, J., & Hang Tuah, U. (2018).
Optimasi Power System Stabilizer (PSS) pada Generator Multi Mesin Untuk Mengurangi Osilasi
Menggunakan Particle Swarm Optimization (PSO). SinarFe7, 1(1), 154–163.
https://journal.fortei7.org/index.php/sinarFe7/article/view/164
Petersén, I., & Eeg-Olofsson, O. (1971). The development of the electroencephalogram in normal
children from the age of 1 through 15 years. Non-paroxysmal activity. Neuropädiatrie, 2(3), 247–304. https://doi.org/10.1055/S-0028-1091786/BIB
Siuly, S., Li, Y., & Zhang, Y. (2016). Electroencephalogram (EEG) and Its Background. 3–21.
https://doi.org/10.1007/978-3-319-47653-7_1
Subha, D. P., Joseph, P. K., Acharya U, R., & Lim, C. M. (2010). EEG signal analysis: a survey. Journal of
Medical Systems, 34(2), 195–212. https://doi.org/10.1007/S10916-008-9231-Z/METRICS
Sun, J., Xu, W., & Liu, J. (2005). Parameter Selection of Quantum-Behaved Particle Swarm
Optimization. Lecture Notes in Computer Science, 3612(PART III), 543–552.
https://doi.org/10.1007/11539902_66
Tharwat, A., & Hassanien, A. E. (2019). Quantum-Behaved Particle Swarm Optimization for
Parameter Optimization of Support Vector Machine. Journal of Classification, 36(3), 576–598.
https://doi.org/10.1007/S00357-018-9299-1/METRICS
Wulansari, R., Contesa Dajamal, E., Darmanto Jurusan Informatika, T., Mipa, F., & Jenderal Achmad Yani Jl Terusan Sudirman, U. (2016). Klasifikasi Sinyal EEG terhadap Rangsangan Suara
Menggunakan Power Spectral Density dan Multilayer Perceptron. Prosiding Sains Nasional Dan Teknologi, 1(1). https://doi.org/10.36499/PSNST.V1I1.1521.
Yang, S., Wang, M., & Jiao, L. (2004). A quantum particle swarm optimization. Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, 1, 320–324.
https://doi.org/10.1109/CEC.2004.1330874
Zhou, N. R., Xia, S. H., Ma, Y., & Zhang, Y. (2022). Quantum particle swarm optimization algorithm with the truncated mean stabilization strategy. Quantum Information Processing, 21(2), 1–23. https://doi.org/10.1007/S11128-021-03380-X/METRICS
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