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Bogaarts G.,AZM Maastricht | Gommer E.,AZM Maastricht | Hilkman D.,AZM Maastricht | van Kranen-Mastenbroek V.,AZM Maastricht | Reulen J.,AZM Maastricht
Epilepsy and Behavior | Year: 2016

The Wada test is commonly used to evaluate language and memory lateralization in candidates for epilepsy surgery. The spatial Brain Symmetry Index (BSI) quantifies inter-hemispheric differences in the EEG. Its application has been shown to be feasible during Wada testing. We developed a method for the quantification of EEG asymmetry that matches visual assessments of the EEG better than BSI. Fifty-three patients' EEG data, with a total of 85 injections were analyzed. In a step-wise, data-driven manner, multiple electrode and frequency band combinations were evaluated. Eventually, BSI, calculated using only the frontal electrodes F3 and F4, was combined with a temporal measure of delta power in the central electrodes, C3 and C4, into a new measure: cBSI. Using the area under the ROC curve (AUC), we showed that cBSI performs significantly better relative to BSI (median AUC 0.98 versus 0.96, p = 0.0015, Wilcoxon signed rank test). Our results showed that asymmetry detection was significantly improved by combining temporal with spatial qEEG measures. In the future, our combined qEEG measure could allow for a more objective way of monitoring EEG asymmetry, thereby increasing the feasibility of using EEG as a monitoring tool during the Wada test. Future studies should, however, validate our cBSI method in real time in the operating room or radiology suite. © 2016 Elsevier Inc.


Bogaarts J.G.,AZM Maastricht | Gommer E.D.,AZM Maastricht | Hilkman D.M.W.,AZM Maastricht | van Kranen-Mastenbroek V.H.J.M.,AZM Maastricht | Reulen J.P.H.,AZM Maastricht
Annals of Biomedical Engineering | Year: 2014

Aim of our project is to further optimize neonatal seizure detection using support vector machine (SVM). First, a Kalman filter (KF) was used to filter both feature and classifier output time series in order to increase temporal precision. Second, EEG baseline feature correction (FBC) was introduced to reduce inter patient variability in feature distributions. The performance of the detection methods is evaluated on 54 multi channel routine EEG recordings from 39 both term and pre-term newborns. The area under the receiver operating characteristics curve (AUC) as well as sensitivity and specificity are used to evaluate the performance of the classification method. SVM without KF and FBC achieves an AUC of 0.767 (sensitivity 0.679, specificity 0.707). The highest AUC of 0.902 (sensitivity 0.801, specificity 0.831) is achieved on baseline corrected features with a Kalman smoother used for training data pre-processing and a KF used to filter the classifier output. Both FBC and KF significantly improve neonatal epileptic seizure detection. This paper introduces significant improvements for the state of the art SVM based neonatal epileptic seizure detection. © 2014, Biomedical Engineering Society.


Bogaarts J.G.,AZM Maastricht | Gommer E.D.,AZM Maastricht | Hilkman D.M.W.,AZM Maastricht | van Kranen-Mastenbroek V.H.J.M.,AZM Maastricht | Reulen J.P.H.,AZM Maastricht
Medical and Biological Engineering and Computing | Year: 2016

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age-independent seizure detection is possible by training one classifier on EEG data from both neonatal and adult patients. Furthermore, our results indicate that for accurate age-independent seizure detection, it is important that EEG data from each age category are used for classifier training. This is particularly important for neonatal seizure detection. Our results underline the under-appreciated importance of training dataset composition with respect to accurate age-independent seizure detection. © 2016 The Author(s)


PubMed | AZM Maastricht
Type: Journal Article | Journal: Annals of biomedical engineering | Year: 2014

Aim of our project is to further optimize neonatal seizure detection using support vector machine (SVM). First, a Kalman filter (KF) was used to filter both feature and classifier output time series in order to increase temporal precision. Second, EEG baseline feature correction (FBC) was introduced to reduce inter patient variability in feature distributions. The performance of the detection methods is evaluated on 54 multi channel routine EEG recordings from 39 both term and pre-term newborns. The area under the receiver operating characteristics curve (AUC) as well as sensitivity and specificity are used to evaluate the performance of the classification method. SVM without KF and FBC achieves an AUC of 0.767 (sensitivity 0.679, specificity 0.707). The highest AUC of 0.902 (sensitivity 0.801, specificity 0.831) is achieved on baseline corrected features with a Kalman smoother used for training data pre-processing and a KF used to filter the classifier output. Both FBC and KF significantly improve neonatal epileptic seizure detection. This paper introduces significant improvements for the state of the art SVM based neonatal epileptic seizure detection.


PubMed | AZM Maastricht
Type: Journal Article | Journal: Medical & biological engineering & computing | Year: 2016

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age-independent seizure detection is possible by training one classifier on EEG data from both neonatal and adult patients. Furthermore, our results indicate that for accurate age-independent seizure detection, it is important that EEG data from each age category are used for classifier training. This is particularly important for neonatal seizure detection. Our results underline the under-appreciated importance of training dataset composition with respect to accurate age-independent seizure detection.

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