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Research on Eit Boundary Measured Voltage Data Denoising Based on a Subspace Method

ABSTRACT Biomedical signals are the concentrated expression of human life information and also the window to peep into the phenomenon of life. The study of the theory and methods of biomedical signal detection and processing had great significance for understanding the laws of motion of human life, exploring new disease prevention and treatment methods, as well as the development of hightech medical equipment industries. Biomedical signal detection and processing almost became the common research area for biomedical engineering. The raw data acquired by Electrical Impedance Tomography (EIT) is the measured boundary voltage data. The boundary voltage is measured as follows: a safe current is applied on the surface of the human body, the voltage on the surface is measured, and the internal impedance distribution and variation is reconstructed via the measured boundary voltage data. For the EIT image reconstruction problem, the measured data contains noise with different levels for some objective reasons, such as the hardware and the measurement way, etc. In this paper, a general framework of subspace method is proposed, and several classic techniques should be induced under this framework. Our experiments are based on real data, and the experimental results show that the subspace method should improve the resolution of the EIT image.