A low-rank matrix factorization approach for joint harmonic and baseline noise suppression in biopotential signals. (April 2017)

• Record Type: Journal Article
• Authors: Zivanovic, Miroslav; Niegowski, Maciej; Lecumberri, Pablo; Gómez, Marisol
• Is Part Of: Computer methods and programs in biomedicine. Volume 141(2017)
• Page Start: 59
• Page End: 71
• Publication Date: 2017-04
• Subjects: Harmonic noise -- Baseline noise -- Low-rank matrix -- Sparsity -- Non-negative matrix factorization; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.01.008
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Harmonic and baseline noise have very sparse time-frequency representations. Low-rank matrix factorization provides compact descriptions of underlying sources in the data. The method is robust to low signal-to-noise ratios. We report a superior performance regarding two state-of-the-art methods. Abstract: Background and objectives: In this paper we propose a novel single-channel harmonic and baseline noise removal approach based on the low-rank matrix factorization theory. It aims to enhance spectrogram sparsity in order to significantly reduce the dimensionality of the underlying sources in the input data. Such a low-rank non-negative representation approach admits efficient noise removal. Methods: The sparsity is improved by a modification of the time-frequency basis through the following signal processing steps: (1) spectrograms segmentation, (2) non-negative rank estimation, and (3) source grouping. The source waveforms are retrieved by means of non-negative matrix factorization and the overlap-add method. The proposed method was tested on real electrocardiogram and electromyogram signals for different analysis scenarios, against two state-of-the-art reference methods. Results: Performance evaluation was carried out by means of the output signal-to-interference ratio. In the electrocardiogram analysis scenarios, for the input signal-to-interference ratio as low as −15 dB, the proposed method outperforms the reference methods by 8 dB and 17 dB respectively. Regarding electromyogram denoising, the performance improvement is about 3 dB. Conclusions: The proposed method was shown to be very efficient in harmonic and baseline simultaneous removing from electrocardiogram and electromyogram signals. Its structure allows for a straightforward extension to other biopotential signals e.g. electroencephalograms and multichannel processing.