Key result
A support vector machine-based automated system detected the electrical onset of temporal lobe seizures using scalp-recorded EEG with 80% sensitivity and 86% specificity.
Why the study?
Does a support vector machine-based system improve the detection of electrical onset of seizures in patients with temporal lobe epilepsy compared to other classifiers?
Does a support vector machine-based system improve the detection of electrical onset of seizures in patients with temporal lobe epilepsy compared to other classifiers?
A support vector machine-based system using scalp-recorded EEG features can effectively detect the electrical onset of seizures in temporal lobe epilepsy, potentially aiding in the management of drug-resistant epilepsy.
May aid automated seizure detection in temporal lobe epilepsy; leaves open prospective validation before clinical adoption.
PURPOSE: To design a non-patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy. METHODS: We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system. RESULTS: Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively. CONCLUSIONS: The support vector machine-based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers. CONCLUSIONS: Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.
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Veerasingam et al. (2018) studied Temporal lobe epilepsy (n=18). Support vector machine (SVM) based seizure detection system vs. Other machine learning classifiers (LDA, NB, DT, KNN) was evaluated on Sensitivity and specificity for detecting electrical onset of seizures. A support vector machine-based automated system detected the electrical onset of temporal lobe seizures using scalp-recorded EEG with 80% sensitivity and 86% specificity.
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