Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition

Bilal, Muhammad; Rizwan, Muhammad; Saleem, Sajid; Khan, Muhammad Murtaza; Alkatheir, Mohammed Saeed; Alqarni, Mohammed A.

doi:10.1109/access.2019.2915609

Cited by 28 publications

(7 citation statements)

References 39 publications

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“…Artifacts in EEG signals might make interpretation difficult and lead to incorrect analytical judgments. Numerous algorithms and preprocessing pipelines have been developed to address the problem of artifact rejection in electrophysiological data [32,49,[63][64][65][66][67]. Each of these algorithms has its own set of strengths and focuses on a different area of artifact rejection than the others.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

et al. 2022

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The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.

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Section: Discussionmentioning

confidence: 99%

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

et al. 2022

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“…Several private databases have also been recorded for epilepsy diagnosis using EEG signals [11,24,16,17,18,22,23,19,20,21]. The European Epilepsy database [25] is a private database which consists of high quality, annotated EEG signals from University of Bonn [2], Freiburg [26], Flint hills, and many multi-modal like MRI imaging data.…”

Section: Private Databasesmentioning

confidence: 99%

Open and free EEG datasets for epilepsy diagnosis

Handa,

Mathur,

Goel

2021

Preprint

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The Epilepsies are a common, chronic neurological disorder affecting more than 50 million individuals across the globe. It is characterized by unprovoked, recurring (similar or different type) seizures which are commonly diagnosed through clinical EEGs. Good-quality, open-access and free EEG data can act as a catalyst for on-going state-of-the-art (SOTA) research works for detection, prediction and management of epilepsy and seizures. They can also aid in improving the quality of life (QOL) of these diseased individuals and contribute research in healthcare multimedia, data analytics and Artificial Intelligence (AI) in personalized medicine. This paper presents widely used, available, open and free EEG datasets available for epilepsy and seizure diagnosis. A brief comparison and discussion of open and private datasets has also been done. Such datasets will help in development and evaluation of automatic computer-aided system in healthcare.

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“…The dynamic mode decomposition (DMD) and derivative approaches, a new matrix decomposition method, that introduced as a solution to problems encountered in fluid flow analysis by Schmidt [30], has recently been used to analyze epileptic EEG signals [31,32].…”

Section: Introductionmentioning

confidence: 99%

Epileptic EEG Classification by Using Advanced Signal Decomposition Methods

Cura¹,

Akan²

2021

Epilepsy - Update on Classification, Etiologies, Instrumental Diagnosis and Treatment

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Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. In this chapter, advanced signal analysis methods such as Empirical Mode Decomposition (EMD), Ensembe (EMD), Dynamic mode decomposition (DMD), and Synchrosqueezing Transform (SST) are utilized to classify epileptic EEG signals. EMD and its derivative, EEMD are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). In this study multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then essential IMFs are selected. Finally, time-and spectral-domain, and nonlinear features are extracted from selected IMFs and classified. DMD is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. We present single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. As a third method, we use the SST representations of seizure and pre-seizure EEG data. Various features are calculated and classified by Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes (NB), Logistic Regression (LR), Boosted Trees (BT), and Subspace kNN (S-kNN) to detect pre-seizure and seizure signals. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.

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Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition

Cited by 28 publications

References 39 publications

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

Open and free EEG datasets for epilepsy diagnosis

Epileptic EEG Classification by Using Advanced Signal Decomposition Methods

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