An algorithm for detecting seizure termination in scalp EEG

Shoeb, Ali; Kharbouch, Alaa; Soegaard, Jacqueline; Schachter, Steven; Guttag, John V.

doi:10.1109/iembs.2011.6090357

Cited by 16 publications

(16 citation statements)

References 5 publications

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“…Spectral and energy features were extracted from a periodogram, which was estimated by applying the Welch algorithm with 50% overlap [14]. Let pth windowed input signal x be represented as [12] x p (n) ∼ = w(n)x(n + pR), n = 0, 1, .…”

Section: Frequency Domainmentioning

confidence: 99%

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Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Prasanna

Subathra

Mohammed

et al. 2021

JPM

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Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.

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Section: Frequency Domainmentioning

confidence: 99%

“…In previous work, the authors proposed a machine learning method for the classification of seizures using scalp EEG and a support vector machine (SVM) classifier, which achieved an accuracy of 90% [14]. A wavelet-based feature extraction technique was performed to extract the statistical feature of the mean absolute deviation (MAD).…”

Section: Introductionmentioning

confidence: 99%

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Prasanna

Subathra

Mohammed

et al. 2021

JPM

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“…In EEG analytics, we usually treat the collected EEG data X ∈ R C×T as features and build models to predict the behavior Y . Existing works on EEG analytics usually rely on manual feature extraction [16][17][18], leading to significant manual efforts. Recent studies [1][2][3][4] introduce deep learning into EEG analytics to automatically learn the features, showing significant improvement in accuracy.…”

Section: Background and Related Workmentioning

confidence: 99%

Saga: Sparse Adversarial Attack on EEG-Based Brain Computer Interface

Feng

Wang

Ding

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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With the recent advancement of the Brain-Computer Interface (BCI), Electroencephalogram (EEG) analytics gain a lot of research attention from various domains. Understanding the vulnerabilities of EEG analytics is important for safely applying this emerging technology in our daily life. Recent studies show that EEG analytics are vulnerable to adversarial attacks when adding small perturbations on the EEG data. However, fewer research efforts have been devoted to the robustness of EEG analytics under sparse perturbations that attack only small portions of the data. In this paper, we conduct the first in-depth study on the robustness of EEG analytics under sparse perturbations and propose the first Sparse Adversarial eeG Attack, SAGA, to identify weakness of EEG analytics. Specifically, by viewing EEG data as time series collected from several channels, we design an adaptive mask to uniformly represent diverse sparsity in adversarial attacks. We further introduce a PGD-based iterative solver to automatically select the time steps and channels under the given sparsity constraints and effectively identify the adversarial examples on EEG data. Extensive experiments show that SAGA can effectively generate sparse perturbations and introduces a 77.02% accuracy drop on average by only perturbing 5% channels and time steps.

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“…One of the challenges in patient-specific epilepsy detection arises due to the dissimilarity among seizure types especially in the training set and those in the unseen (test) data. To cope with this problem and to suppress false alarms, most of the proposed epilepsy detection techniques in the literature randomly pick a large portion of EEG record as the training dataset which is infeasible in practice (Greene et al, 2008;Mirowski, Madhavan, LeCun, & Kuzniecky, 2009;Shoeb, Kharbouch, Soegaard, Schachter, & Guttag, 2011;Yan et al, 2015). In a recent study Kiranyaz, Ince, Zabihi, and Ince (2014), an automated epilepsy detection system was proposed utilizing a high dimensional feature set containing a large number of low-level features.…”

Section: Introductionmentioning

confidence: 98%