Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm

Schimmack, Manuel; Mercorelli, Paolo

doi:10.1515/eletel-2016-0002

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…They reported better results using the db4 at level 3. These results appear to be consistent with the literature, since most studies have reported using either the db2 [ 73 , 74 , 75 , 76 , 77 , 78 , 79 ] or the db4 [ 67 , 80 , 81 , 82 , 83 , 84 ]. In terms of the optimal decomposition level, level 4 appears to be the most widely used [ 5 , 19 , 71 , 77 , 82 , 85 , 86 ], while the level 3 is also frequently chosen [ 25 , 72 , 87 , 88 , 89 , 90 ].…”

Section: Contaminant Reduction Methodssupporting

confidence: 92%

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

Boyer

Bouyer

Roy

et al. 2023

Sensors

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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.

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Section: Contaminant Reduction Methodssupporting

confidence: 92%

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

Boyer

Bouyer

Roy

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…They reported better results using the db4 at level 3. These results appear to be consistent with the literature, since most studies have reported using either the db2 [73][74][75][76][77][78][79] or the db4 [67,[80][81][82][83][84]. In terms of optimal decomposition level, level 4 appears to be the most widely used [5,19,71,77,82,85,86], while the level 3 is also frequently chosen [25,72,[87][88][89][90].…”

Section: Denoising Methods After Wavelet Decompositionsupporting

confidence: 87%

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

Boyer¹,

Bouyer²,

Roy³

et al. 2023

Preprint

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EMG analysis is becoming increasingly important 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, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

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“…Unlike DWT, Wavelet Packet Transform (WPT) has the advantage of denoising on frequency bands of the same width. WPT denoising has been applied in various fields including speech enhancement [26], [27], noise detection in bio-signals [28] and atomic force microscopy image denoising [29]. WPT has proven to be particularly suitable for background denoising with a specific frequency content.…”

Section: Related Workmentioning

confidence: 99%

“…For simplicity, we keep using then the small architectures from Table I. Indeed, we also tried three architectures with number of parameters around n p = 8670: [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] with n p = 5488, [8-16-32] with n p = 6632 and with n p = 21216. The best performing is the with S p = 19.3 and S r = 19.5 for the first fold.…”

Section: B Choice Of the Nn Architectures: Airport Background Removalmentioning

confidence: 99%

Robust Time Series Denoising with Learnable Wavelet Packet Transform

Frusque¹,

Fink²

2022

Preprint

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In many applications, signal denoising is often the first pre-processing step before any subsequent analysis or learning task. In this paper, we propose to apply a deep learning denoising model inspired by a signal processing, a learnable version of wavelet packet transform. The proposed algorithm has signficant learning capabilities with few interpretable parameters and has an intuitive initialisation. We propose a post-learning modification of the parameters to adapt the denoising to different noise levels. We evaluate the performance of the proposed methodology on two case studies and compare it to other state of the art approaches, including wavelet schrinkage denoising, convolutional neural network, autoencoder and U-net deep models. The first case study is based on designed functions that have typically been used to study denoising properties of the algorithms. The second case study is an audio background removal task. We demonstrate how the proposed algorithm relates to the universality of signal processing methods and the learning capabilities of deep learning approaches. In particular, we evaluate the obtained denoising performances on structured noisy signals inside and outside the classes used for training. In addition to having good performance in denoising signals inside and outside to the training class, our method shows to be particularly robust when different noise levels, noise types and artifacts are added.

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Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm

Cited by 5 publications

References 12 publications

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

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

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

Robust Time Series Denoising with Learnable Wavelet Packet Transform

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