A low-rank matrix factorization approach for joint harmonic and baseline noise suppression in biopotential signals

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.

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.

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 -15dB, the proposed method outperforms the reference methods by 8dB and 17dB respectively. Regarding electromyogram denoising, the performance improvement is about 3dB.

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.