Removing harmonic power line interference from biopotential signals in low cost acquisition systems

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.

The stationary wavelet transform as an efficient reductor of powerline interference for atrial bipolar electrograms in cardiac electrophysiology

OBJECTIVE

The most relevant source of signal contamination in the cardiac electrophysiology (EP) laboratory is the ubiquitous powerline interference (PLI). To reduce this perturbation, algorithms including common fixed-bandwidth and adaptive-notch filters have been proposed. Although such methods have proven to add artificial fractionation to intra-atrial electrograms (EGMs), they are still frequently used. However, such morphological alteration can conceal the accurate interpretation of EGMs, specially to evaluate the mechanisms supporting atrial fibrillation (AF), which is the most common cardiac arrhythmia. Given the clinical relevance of AF, a novel algorithm aimed at reducing PLI on highly contaminated bipolar EGMs and, simultaneously, preserving their morphology is proposed.

APPROACH

The method is based on the wavelet shrinkage and has been validated through customized indices on a set of synthesized EGMs to accurately quantify the achieved level of PLI reduction and signal morphology alteration. Visual validation of the algorithm's performance has also been included for some real EGM excerpts.

MAIN RESULTS

The method has outperformed common filtering-based and wavelet-based strategies in the analyzed scenario. Moreover, it possesses advantages such as insensitivity to amplitude and frequency variations in the PLI, and the capability of joint removal of several interferences.

SIGNIFICANCE

The use of this algorithm in routine cardiac EP studies may enable improved and truthful evaluation of AF mechanisms.

A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation

OBJECTIVE

The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology.

APPROACH

The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed.

MAIN RESULTS

In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, since f-waves remained almost intact after removing very high levels of noise.

SIGNIFICANCE

The proposed algorithm may facilitate a reliable characterization of the f-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF.

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

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

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.

Fast and effective estimation of narrowband components for bioelectrical signals

In this paper we propose a novel approach for estimating narrowband components from bioelectrical signals. The approach is based on the notion of modulated quadratic variation, introduced as a measure of variability for narrowband signals. The algorithm is the closed-form solution to a constrained convex optimization problem, where narrowband components are estimated tracking the slow variations around a central frequency in the measured signal. The approach is general and can be applied to any bioelectrical signal, either for diagnostic or denoising purposes. In this paper we assess its performance on ECG and EMG signals. Numerical results show its effectiveness in removing narrowband artifacts, such as power-line interference, while preserving signal morphology. It greatly outperforms conventional notch filtering. Moreover, it is also very fast, as its computational complexity is linear in the size of the vector to process.

Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander

We present a novel approach to single-channel power line interference (PLI) and baseline wander (BW) removal from surface electromyograms (EMG). It is based on non-negative matrix factorization (NMF) using a priori knowledge about the interferences. It performs a linear decomposition of the input signal spectrogram into non-negative components, which represent the PLI, BW and EMG spectrogram estimates. They all exhibit very different time-frequency patterns: PLI and BW are both sparse whereas EMG is noise-like. Initialization of the classical NMF algorithm with accurately designed PLI, BW and EMG structures and a carefully adjusted matrix decomposition rank increases the separation performance. The comparative study suggests that the proposed method outperforms two state-of-the-art reference methods.

Fetal ECG extraction from abdominal signals: a review on suppression of fundamental power line interference component and its harmonics

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

Simultaneous powerline interference and baseline wander removal from ECG and EMG signals by sinusoidal modeling

We present a compact approach to joint modeling of powerline interference (PLI) and baseline wonder (BW) for denoising of biopotential signals. Both PLI and BW are modeled by a set of harmonically related sinusoids modulated by low-order time polynomials. The sinusoids account on the harmonicity and mean instantaneous frequency of the PLI in the analysis window, while the polynomials capture the frequency and amplitude deviations from their nominal values and characterize the BW at the same time. The resulting model is linear-in-parameters and the solution to the corresponding linear system is estimated in a simple and efficient way through linear least-squares. The proposed modeling method was evaluated on real electrocardiographic (ECG) and electromyographic (EMG) signals against three reference methods for different analysis scenarios. The comparative study suggests that the proposed method outperforms the reference methods in terms of residual interference energy in the denoised biopotential signals.