EEG-based seizure detection in patients with intellectual disability: Which EEG and clinical factors are important?

Wang, Lei; Long, Xi; Aarts, Ronald M.; Dijk, Hans van; Arends, Johan

doi:10.1016/j.bspc.2018.12.003

Cited by 9 publications

(4 citation statements)

References 57 publications

(92 reference statements)

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“…Since focal seizures often affect a smaller brain region, the changes of ACS (i.e., average PS level) may be not significant across patients. Another reason may be the regular occurrence of a slow activity (i.e., seizure imitators) in the background EEG of patients with an ID [26].…”

Section: Discussionmentioning

confidence: 99%

“…As a result, the performance of automated seizure detection for ID patients remains unclear. Our previous study [26] shows that the automated detection is difficult for minor seizures that show blurry boundaries associated with abnormal background EEG signals. The traditional EEG feature-based seizure detection methods [20,22,27] may have limitations on detecting these minor seizures because they do not 28.9 ± 13.7 12-51…”

Section: Introductionmentioning

confidence: 99%

“…• Instead of treating all seizure types as an entity (a common pitfall), we evaluated seizures according to EEG discharge patterns due to the heterogeneous EEG characteristics of the ID population [26]. • Instead of using manually-selected, artifact-free EEG sessions (often from long-term interictal recordings), we used randomlyselected, continuous EEG sessions to represent real-life interictal EEG signals.…”

Section: Introductionmentioning

confidence: 99%

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A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

Wang

Long

Aarts

et al. 2019

Biomedical Signal Processing and Control

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

Wang

Long

Aarts

et al. 2019

Biomedical Signal Processing and Control

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“…We extracted the commonly used time-frequency and the morphological EEG features. For the features in the time-frequency domain, we calculated wavelet decomposition (WD) coefficients (Sharma et al 2014), the mean and standard deviation of distances between signal peaks and troughs, and the signal sample entropy (Wang et al 2019). The morphological features were extracted using VG methods (Wang et al 2017b).…”

Section: Features Extractionmentioning

confidence: 99%

False alarms reduction in non-convulsive status epilepticus detection via continuous EEG analysis

et al. 2020

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Objective: Frequent false alarms from computer-assisted monitoring systems may harm the safety of patients with non-convulsive status epilepticus (NCSE). In this study, we aimed at reducing false alarms in the NCSE detection based on preventing from three common errors: over-interpretation of abnormal background activity, dense short ictal discharges and continuous interictal discharges as ictal discharges. Approach: We analyzed 10 participants' hospital-archived 127-hour electroencephalography (EEG) recordings with 310 ictal discharges. To reduce the false alarms caused by abnormal background activity, we used morphological features extracted by visibility graph methods in addition to time-frequency features. To reduce the false alarms caused by over-interpreting short ictal discharges and interictal discharges, we created two synthetic classes-'Suspected Non-ictal' and 'Suspected Ictal'-based on the misclassified categories and constructed a synthetic 4-class dataset combining the standard two classes-'Non-ictal' and 'Ictal'-to train a 4-class classifier. Precision-recall curves were used to compare our proposed 4-class classification model and the standard 2-class classification model with or without the morphological features in the leave-one-out cross validation stage. The sensitivity and precision were primarily used as performance metrics for the detection of a seizure event. Main results: The 4-class classification model improved the performance of the standard 2-class model, in particular increasing the precision by 15% at an 80% sensitivity level when only time-frequency features were used. Using the morphological features, the 4-class classification model achieved the best performances: a sensitivity of 93% ± 12% and a precision of 55% ± 30% in the group level. 100% accuracy was reached in a participant's 4.3-hour recording with 5 ictal discharges. Significance: False alarms in the NCSE detection were remarkably reduced using the morphological features and the proposed 4-class classification model.

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Weighted directed graph-based automatic seizure detection with effective brain connectivity for EEG signals

Sun,

Liu,

2023

SIViP

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Epileptic seizure is one of the most common neurological disorders characterized by sudden abnormal discharge of neurons in the brain. Automated seizure detection using electroencephalograph (EEG) recordings would improve the quality of treatment and reduce medical overhead. The purpose of this paper is to design an automated seizure detection framework that can effectively identify seizure and non-seizure events by discovering connectivity between brain regions. In this work, a weighted directed graph-based method with effective brain connectivity (EBC) is proposed for seizure detection. The weighted directed graph is built by analyzing the correlation among the different regions of the brain. Then, graph theory-based measures are used to extract features for classification. Furthermore, we illustrate the ability of the proposed method to achieve seizure detection for the patient-specific model and the cross-patient model. The results show that the proposed method achieves accuracy values of 99.97% and 98.29% for the patient-specific model and the cross-patient model in the CHB-MIT dataset, respectively. These results demonstrate that the proposed method achieves an effective classification performance and can be used to provide assistance for automatic seizure detection and clinical diagnosis.

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EEG-based seizure detection in patients with intellectual disability: Which EEG and clinical factors are important?

Cited by 9 publications

References 57 publications

A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

False alarms reduction in non-convulsive status epilepticus detection via continuous EEG analysis

Weighted directed graph-based automatic seizure detection with effective brain connectivity for EEG signals

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