Self-training dictionary based approximated ℓ0 norm constraint reconstruction for compressed ECG. (July 2021)

• Record Type: Journal Article
• Authors: Liu, Shengxing; Wu, Fei-Yun
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Compressed sensing (CS) -- Dictionary learning -- Sparse reconstruction -- Approximated ℓ0 norm constraint; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102768
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Compressed sensing provides a sub-Nyquist sampling rate but requires a sparse structure of the signal. Electrocardiogram (ECG) signal expresses no sparse structure in time domain. This study aims to improve the sparse expression based on a self-training dictionary scheme (STDS), to obtain a self-training dictionary. Furthermore, an approximated ℓ0 norm constraint method (AL0CM) is designed for sparse expression by using an accelerated gradient descent method. The reconstruction of the compressed data could be applied by combining the sampling matrix, dictionary, and sparse expression. The ECG experiments are conducted to confirm the superiority of the proposed STDS-AL0CM framework in terms of the recovered signal-to-noise ratio (RSNR), percent norm difference (PND), and running time.