Epileptic EEG Signal Denoising Enhancement Using Improved Threshold Based Wavelet Method

Divya, P.; Devi, B. Aruna

doi:10.1109/icscan53069.2021.9526343

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Hard thresholding causes discontinuity, while soft thresholding suffers from deviation issues. Hence, novel approaches, such as grand-based adaptive algorithms and functions related to the decomposition level, have been introduced [51], [52]. Particularly for cognitive tasks, Rigsure hard thresholding demonstrated optimal performance [50].…”

Section: ) Semi-automated Basic Preprocessingmentioning

confidence: 99%

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Kyriaki,

Koukopoulos,

Fidas

2024

IEEE Access

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Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment.Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users' cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.

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Section: ) Semi-automated Basic Preprocessingmentioning

confidence: 99%

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Kyriaki,

Koukopoulos,

Fidas

2024

IEEE Access

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“…However, the hard threshold leads to oscillation of the reconstructed ECG, and the soft threshold may reduce the amplitude of the ECG features. The approaches in [12], [14], [15] proposed an improved threshold to eliminate noise from ECG. The improved threshold not only avoided the reconstructed ECG oscillation but also effectively preserved the original features of the ECG.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Denoising Autoencoder Design for Noise Removal in Electrocardiography

et al. 2022

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This study proposes two denoising autoencoder (DAE) models with discrete cosine transform (DCT) and discrete wavelet transform (DWT), namely DCT-DAE and DWT-DAE, to remove electrode motion artifacts in noisy electrocardiography (ECG). Initially, the discrete cosine transform and discrete wavelet transform efficiently removed the high-frequency noise. The six encoder layers then retain important ECG features while the six decoder layers reconstruct the clean ECG. To improve the denoising performance, two network layers, the residual block and pixel adjustment, are added to the encoder and decoder layers to solve the vanishing gradient and improve subtle feature extraction. The proposed methods were applied to 66,000 real-recorded noisy ECG fragments. The experimental result indicates that DWT-DAE and DCT-DAE can improve the signal-to-noise ratio by 25.29 and 25.13 dB on average when the input signal-to-noise ratio is -6 dB.

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Novel Preprocessing of Multimodal Condition Monitoring Data for Classifying Induction Motor Faults Using Deep Learning Methods

Hejazi

Packianather

Liu

2022

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)

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Epileptic EEG Signal Denoising Enhancement Using Improved Threshold Based Wavelet Method

Cited by 4 publications

References 6 publications

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Lightweight Denoising Autoencoder Design for Noise Removal in Electrocardiography

Novel Preprocessing of Multimodal Condition Monitoring Data for Classifying Induction Motor Faults Using Deep Learning Methods

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