ECG-EMG separation by using enhanced non-negative matrix factorization

Niegowski, Maciej; Zivanovic, Miroslav

doi:10.1109/embc.2014.6944553

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…There are many ways to create such a multi-modal system [ 284 ]. One of the most commonly applied methods is EEG monitoring, which can be combined with other measurement methods [ 28 , 285 , 286 , 287 , 288 , 289 , 290 , 291 ]: brain imaging techniques, such as MRI and fNIRS; biological signals, such as ECG and EMG; brain stimulation techniques, such as trans-cranial magnetic stimulation (TMS) and trans-cranial direct current stimulation (tDCS). Nevertheless, the multi-modal neurological imaging and/or monitoring is associated with specific signal processing and data analyses challenges, such as inter alia [ 20 , 292 , 293 , 294 , 295 , 296 , 297 ]: the EEG may obtain artifacts from other biological signals (such as EMG) or be distorted by the noise produced by accompanied devices for imaging (such as MRI) or stimulation (such as TMS).…”

Section: Discussionmentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

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Section: Discussionmentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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“…NMFs applied to the spectrograms of physiological signals are of particular interest for identifying signals exhibiting similar temporal behaviors [22], since their shared factors are highly correlated. With this in mind, a procedure enabling the elimination of high-energy noises from noisy PCGs using synchronous ECGs was proposed in [14].…”

Section: Stftmentioning

confidence: 99%

Phonocardiogram Signal Denoising Based on Nonnegative Matrix Factorization and Adaptive Contour Representation Computation

Pham

Meignen

Dia

et al. 2018

IEEE Signal Process. Lett.

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This letter introduces a new technique for phonocardiogram (PCG) signal denoising based non-negative matrix factorization (NMF) of its spectrogram and adaptive contour representation computation (ACRC) of its short-time Fourier transform (STFT). More precisely, NMFs on PCG and synchronous electrocardiogram (ECG) spectrograms are first used to filter out high-energy noises from PCG. Then, ACRC is performed on a low-pass filtered version of the STFT of the resulting signal to identify relevant time-frequency (TF) components which are subsequently used for signal retrieval. Numerical experiments conducted on a real database of noisy PCG signals (SiSEC2016) illustrate the superiority of the proposed method over state-ofthe-art techniques.

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“…The focus on this paper is to demonstrate the feasibility of source separation in ECG signals in order to detect R peaks, using a blind source separation algorithm called Non-negative Matrix Factorization (NMF). Some researches have been made combining electrocardiogram and NMF, such as ECG-EMG separation [5] or extraction of fetal ECG [6], but to the best of our knowledge, none on the separation of the ECG waves.…”

Section: Introductionmentioning

confidence: 99%

R-Peak Detection in Holter ECG Signals Using Non-Negative Matrix Factorization

Guyot¹,

Voiriot²,

Djermoune³

et al. 2018

Computing in Cardiology Conference (CinC)

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Holter monitoring is mainly used for medical followup and diagnosis of patients with suspected cardiac arrhythmia such as heart rhythm irregularities that can be missed during classical electrocardiogram recording (ECG). However, these long-term continuous recordings represent a large amount of data that cannot be processed by hand. In this article, we present a new method based on Non-negative Matrix Factorization (NMF) to detect Rpeaks in Holter signals. The approach consists in two stages: source separation based on the different timefrequency patterns of the QRS complexes and the other waves of the signal (P and T waves) and R-peak detection using Automatic Objective Thresholding (AOT). The proposed approach is validated on the MIT-BIH Arrhythmia database and achieves an average sensitivity of 99.59% and a precision of 99.69%. Using the MIT-BIH Noise Stress Test database, we also show the ability of our approach to discriminate R-peaks in signals contaminated with different noises.

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ECG-EMG separation by using enhanced non-negative matrix factorization

Cited by 10 publications

References 12 publications

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Phonocardiogram Signal Denoising Based on Nonnegative Matrix Factorization and Adaptive Contour Representation Computation

R-Peak Detection in Holter ECG Signals Using Non-Negative Matrix Factorization

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