Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

Marlin Ramadhan Baidillah (Graduate School of Science & Engineering, Dept. Mechanical Eng., Div. Fundamental Eng., Chiba University)
Zengfeng Gao (Graduate School of Science & Engineering, Dept. Mechanical Eng., Div. Fundamental Eng., Chiba University)
Al-Amin Saichul Iman (Graduate School of Science & Engineering, Dept. Mechanical Eng., Div. Fundamental Eng., Chiba University)
Masahiro Takei (Graduate School of Science & Engineering, Dept. Mechanical Eng., Div. Fundamental Eng., Chiba University)

Abstract


Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.


Keywords


Electrical impedance tomography (EIT); Adaptive Noise cancellation; FPGA-based EIT system; Time-varying noise model; Adaptive filter

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References


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DOI: https://doi.org/10.30564/ese.v1i2.1043

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