Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

Shahbakhti, Mohammad; Maugeon, Maxime; Beiramvand, Matin; Marozas, Vaidotas

doi:10.3390/brainsci9120352

Cited by 19 publications

(5 citation statements)

References 47 publications

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“…The segments that contained eye-blink artifacts and transient muscular activity were selected and removed as confirmed by an expert (i.e., neuroelectrophysiologist) [73]. The presence of EOG artifacts may be indicated by a higher absolute value of skewness, as it has a significantly larger amplitude compared to an uncontaminated EEG signal [74].…”

Section: ) Eeg Data Filtering and Artifact Removalmentioning

confidence: 99%

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Noor¹,

Ibrahim²,

Lah³

et al. 2021

IEEE Access

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Reliable prediction of traumatic brain injury (TBI) outcomes based on machine learning (ML) that is derived from quantitative electroencephalography (EEG) features has renewed interest in recent years. Nevertheless, the approach has suffered from imbalanced datasets. Hence, to get a reliable predictive model for predicting outcomes, specifically in a high proportion of moderate TBI with good outcomes, could be challenging. This work proposes an improved outcome predictive model that combines the absolute power spectral density (PSD) as input features for training random under-sampling boosting decision trees (RUSBoosted Trees) as a classifier. Resting-state, eyes-closed EEG data were obtained from 27 moderate TBI patients with follow-up visits. Patient outcome at 4-10 weeks to 12-month was dichotomized based on the Glasgow Outcome Scale as poor (GOS score ≤ 4) and good outcomes (GOS score = 5). The predictive values of absolute PSD from five frequency bands: δ (0.5-4Hz), θ (4-7Hz), α (7-13Hz), β (13-30Hz) and γ (30-100Hz) were evaluated to identify the most informative predictors for reliable prediction outcomes. RUSBoosted Trees performed best at discriminating patients into two outcomes categories (G-Mean = 92.95%, TP rate =100%, TN rate = 86.4%) of absolute PSD in δ and γ bands, which was excellent compared to the other state-of-the-art methods. The highest area under the curve (AUC) of absolute PSD in δ (AUC δ = 0.97) and γ (AUC γ = 0.95) revealed their predictive values as robust prognostic markers for prediction outcomes. The RUSBoosted Trees presents a promising result in prognosis prediction of highly imbalanced data, making it an accessible prediction tool for clinical decision-making, unlike the black-box approaches.

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Section: ) Eeg Data Filtering and Artifact Removalmentioning

confidence: 99%

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Noor¹,

Ibrahim²,

Lah³

et al. 2021

IEEE Access

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show abstract

“…However, this manual detection process was time-consuming and di cult as well, especially for long-time EEG recordings. Besides, manual detection was not applicable for real-time EEG applications [12]. These limitations led to develop the automatic approaches of eye-blink artifact detection.…”

Section: Introductionmentioning

confidence: 99%

Quantitative EEG features and machine learning classifiers for eye-blink artifact detection: A comparative study

Rashida

Habib

2023

Neuroscience Informatics

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“…In [15], the maximum overlap wavelet transform decomposition (MODWT) is used to perform timefrequency analysis on EEGs, which has been applied to filter single channel eye blinks and muscle artifacts. Based on the stationary wavelet transform (SWT), a method [16] on signal skewness has been developed to remove eye blinks. But due to the influence of individual differences, the skewness threshold for each individual fluctuates significantly, leading to poor performance.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Feature Optimization Based Eye Blink Detection Under Epileptiform Discharges

Wang

Cui

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objectives: Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. Methods: In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multidimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. Results: Experiments on EEGs of 16 subjects from the Children's Hospital of Zhejiang University School of Medi-Manuscript

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Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

Cited by 19 publications

References 47 publications

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Quantitative EEG features and machine learning classifiers for eye-blink artifact detection: A comparative study

Multidimensional Feature Optimization Based Eye Blink Detection Under Epileptiform Discharges

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