Denoising method for capillary electrophoresis signal via learned tight frame. Issue 4 (1st June 2020)

• Record Type: Journal Article
• Authors: Lu, Yixiang; Wang, Zhenya; Gao, Qingwei; Sun, Dong; Bao, Hua
• Is Part Of: IET signal processing. Volume 14:Issue 4(2020)
• Page Start: 189
• Page End: 198
• Publication Date: 2020-06-01
• Subjects: signal denoising -- learning (artificial intelligence) -- approximation theory -- electrophoresis -- random noise -- inverse transforms; capillary electrophoresis signal -- learned tight frame -- random noise -- one‐dimensional discrete tight frame -- input signal -- tight frame constraint -- dictionary learning model -- denoised CE signal -- simulated CE signals; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2019.0242
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : Since capillary electrophoresis (CE) signals are always contaminated by random noise, which has negative influence on the accuracy of detection and analysis, it is necessary to remove noise before further applications of the CE signals. In this study, a tight frame learned from the data itself is applied to the removal of noise for CE signals. To achieve an effective decomposition of the CE signal, a one‐dimensional discrete tight frame tailored to the input signal is first constructed by introducing tight frame constraint into the popular dictionary learning model. Then, due to each subband containing different information of the noise, an adaptive threshold is computed to shrink the detail coefficients instead of using a global threshold. Finally, the denoised CE signal is reconstructed from the thresholded coefficients by using the inverse transform of the tight frame. To evaluate the denoising efficiency, the proposed method is applied to the simulated CE signals and real CE signals. Experimental results indicate that compared with other denoising methods, the proposed method obtains a better shape preservation of the peaks as well as a higher signal‐to‐noise ratio.