Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum

Cura, Ozlem Karabiber; Akan, Aydın

doi:10.1016/j.bbe.2020.11.002

Cited by 13 publications

(10 citation statements)

References 41 publications

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“…There are three main sources of information that can be extracted from EEG recordings, which are spatial estimation (e.g., for multichannel EEG), spectral estimation (i.e., power in frequency band) and temporal information (i.e., time windows-based analysis) [27]. The spectral estimation [25], [28], [29], [47], is regularly used in a very wide range of signals that exhibit oscillatory and rhythmic patterns, and it is of particular importance in the analysis of EEG. A comparison of power spectral density (PSD) estimation methods for EEG is shown in work by Musson and Li [30].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Hence, these spectral features include the information about frequency distribution embedded in EEG signals (Degirmenci et al, 2022c ). The different oscillations are embedded in EEG signals which are known to be liable for various cognitive brain functions (Cura and Akan, 2021 ). These are known as delta ( δ ), theta ( θ ), alpha ( α ), beta ( β ), and gamma (γ) waves.…”

Section: Methodsmentioning

confidence: 99%

“…The training process is carried out based on learning a set of decision rules. A leaf node is created when the decision is made, whereas a decision node which is another branch is generated when the decision is not definite (Cura and Akan, 2021 ). In the decision tree-based classification process, the fine, medium, and coarse algorithms are used for this study.…”

Section: Methodsmentioning

confidence: 99%

“…However, all features have the same effect value on classification, which means each of the features has an equal weight in the training (Tzallas et al, 2009 ). It is the mostly preferred algorithm in machine learning approaches due to its simple calculation mechanism created by the non-realistic approach (Cura and Akan, 2021 ; Sayilgan et al, 2021a ; Degirmenci et al, 2022b ). The Gaussian and Kernel algorithms of this Naive Bayes classifier were computed for this study.…”

Section: Methodsmentioning

confidence: 99%

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Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Degirmenci

Yüce²,

Perc

et al. 2023

Front. Hum. Neurosci.

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In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.

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“…Nonetheless, EMD cannot handle multi-channel signals. Cura and Akan ( 2021 ) proposed a single- and multi-channel EEG-based dynamic pattern decomposition (DMD) method to analyze epileptic signals. They extracted high-order spectral moments and subband powers to detect seizure.…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers

Zeng

et al. 2023

Front. Neurosci.

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IntroductionIn the clinical setting, it becomes increasingly important to detect epileptic seizures automatically since it could significantly reduce the burden for the care of patients suffering from intractable epilepsy. Electroencephalography (EEG) signals record the brain's electrical activity and contain rich information about brain dysfunction. As a non-invasive and inexpensive tool for detecting epileptic seizures, visual evaluation of EEG recordings is labor-intensive and subjective and requires significant improvement.MethodsThis study aims to develop a new approach to recognize seizures automatically using EEG recordings. During feature extraction of EEG input from raw data, we construct a new deep neural network (DNN) model. Deep feature maps derived from layers placed hierarchically in a convolution neural network are put into different kinds of shallow classifiers to detect the anomaly. Feature maps are reduced in dimensionality using Principal Component Analysis (PCA).ResultsBy analyzing the EEG Epilepsy dataset and the Bonn dataset for epilepsy, we conclude that our proposed method is both effective and robust. These datasets vary significantly in the acquisition of data, the formulation of clinical protocols, and the storage of digital information, making processing and analysis challenging. On both datasets, extensive experiments are performed using a cross-validation by 10 folds strategy to demonstrate approximately 100% accuracy for binary and multi-category classification.DiscussionIn addition to demonstrating that our methodology outperforms other up-to-date approaches, the results of this study also suggest that it can be applied in clinical practice as well.

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Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum

Cited by 13 publications

References 41 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

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers

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