Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review

Daud, Syarifah Noor Syakiylla Sayed; Sudirman, Rubita

doi:10.1007/s10439-022-03053-5

Cited by 21 publications

(4 citation statements)

References 88 publications

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“…A small number of studies use the wavelet transform (WT). The wavelet has good time-frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently (Daud and Sudirman, 2022). This method has good performance in the spectral analysis of irregular and non-stationary signals within different size windows (Al-Fahoum and Al-Fraihat, 2014).…”

Section: Spectral Power Analysismentioning

confidence: 99%

Application of EEG in migraine

Zhang

Pan

Chen

et al. 2023

Front. Hum. Neurosci.

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Migraine is a common disease of the nervous system that seriously affects the quality of life of patients and constitutes a growing global health crisis. However, many limitations and challenges exist in migraine research, including the unclear etiology and the lack of specific biomarkers for diagnosis and treatment. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity. With the updating of data processing and analysis methods in recent years, EEG offers the possibility to explore altered brain functional patterns and brain network characteristics of migraines in depth. In this paper, we provide an overview of the methodology that can be applied to EEG data processing and analysis and a narrative review of EEG-based migraine-related research. To better understand the neural changes of migraine or to provide a new idea for the clinical diagnosis and treatment of migraine in the future, we discussed the study of EEG and evoked potential in migraine, compared the relevant research methods, and put forwards suggestions for future migraine EEG studies.

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Section: Spectral Power Analysismentioning

confidence: 99%

Application of EEG in migraine

Zhang

Pan

Chen

et al. 2023

Front. Hum. Neurosci.

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“…The wavelet filter decomposes the EIT signal in a detail part (representing the high-frequency information) and an approximation part (representing low-frequency information). The decomposition process of the approximation part is repeated at multiple levels, each level providing more detailed information about the signal's frequency (Khawaja 2007, Daud andSudirman 2022). MODWT differs from other discrete wavelet transform methods as each level of decomposition overlaps with adjacent scales to provide a more comprehensive representation of the signal's spectral content.…”

Section: Filter Algorithmsmentioning

confidence: 99%

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings

Wisse,

Somhorst,

Behr

et al. 2024

Physiol. Meas.

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Objective: Electrical Impedance Tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters. Approach: Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated 1) in the time domain 2) in the frequency domain 3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from fifteen adult and neonatal ICU patients. Main result: Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data. Significance: Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question. 

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“…Denoising algorithms should be carefully chosen or modified depending on the type of noise, as some noise types may respond better to certain denoising techniques than to others. When compared to vSLAM algorithms that do not use denoising techniques, denoising techniques may introduce artefacts that affect the algorithms and potentially result in lower accuracy [20]. It's critical to find a balance between noise reduction and maintaining important visual elements.…”

Section: Related Workmentioning

confidence: 99%

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

Theodorou,

Velisavljevic,

Dyo

et al. 2024

IEEE Access

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This paper undertakes a comprehensive investigation that surpasses the conventional examination of signal enhancement techniques and their effects on visual Simultaneous Localization and Mapping (vSLAM) performance across diverse scenarios. Going beyond the conventional scope, the study extends its focus towards the seamless integration of signal enhancement techniques, aiming to achieve a substantial enhancement in the overall vSLAM performance. The research not only delves into the assessment of existing methods but also actively contributes to the field by proposing innovative denoising techniques that can play a pivotal role in refining the accuracy and reliability of vSLAM systems. This multifaceted approach encompasses a thorough exploration of the intricate relationships between signal enhancement, denoising strategies, their cumulative impact on the performance of vSLAM in real-world applications and the innovative use of Generative Adversarial Networks (GANs) for image inpainting. The GANs effectively fill in missing spaces following object detection and removal, presenting a novel state-of-theart approach that significantly enhances overall accuracy and execution speed of vSLAM. This paper aims to contribute to the advancement of vSLAM algorithms in real-world scenarios, demonstrating improved accuracy, robustness, and computational efficiency through the amalgamation of signal enhancement and advanced denoising techniques.INDEX TERMS signal enhancement, denoising techniques, visual SLAM, object detection, simultaneous localization and mapping(SLAM), object detection and generative adversarial network (GAN).

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Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review

Cited by 21 publications

References 88 publications

Application of EEG in migraine

Application of EEG in migraine

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

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