Numerous nonepileptic paroxysmal events, such as syncope and psychogenic nonepileptic seizures, may imitate seizures and impede diagnosis. Misdiagnosis can lead to mistreatment, affecting patients’ lives considerably. Electroencephalography is commonly used for diagnosing epilepsy. Although on electroencephalograms (EEGs), epileptiform discharges (ED) specifically indicate epilepsy, only approximately 50% of patients with epilepsy have ED in their first EEG. In this study, we developed a deep convolutional neural network (ConvNet)-based classifier to distinguish EEG between patients with epilepsy without ED and controls. Overall, 25 patients with epilepsy without ED in their EEGs and 25 age-matched patients with Tourette syndrome or syncope were enrolled. Their EEGs were classified using the deep ConvNet. When the EEG data without overlapping were used, the accuracy, sensitivity, and specificity were 65.00%, 48.00%, and 82.00%, respectively. The performance measures improved when the input EEG data were augmented through overlapping. With 95% EEG data overlapping, the accuracy, sensitivity, and specificity increased to 80.00%, 70.00%, and 90.00%, respectively. The proposed method could be regarded as a pilot study to demonstrate a proof of concept of a potential diagnostic value of deep ConvNet in patients with epilepsy without ED. Further studies are needed to assist neurologists in distinguishing nonepileptic paroxysmal events from epilepsy.

We develop a neurofuzzy network technique to extract TSK-type fuzzy rules from a given set of input-output data for system modeling problems. Fuzzy clusters are generated incrementally from the training dataset, and similar clusters are merged dynamically together through input-similarity, output-similarity, and output-variance tests. The associated membership functions are defined with statistical means and deviations. Each cluster corresponds to a fuzzy IF-THEN rule, and the obtained rules can be further refined by a fuzzy neural network with a hybrid learning algorithm which combines a recursive singular value decomposition-based least squares estimator and the gradient descent method. The proposed technique has several advantages. The information about input and output data subspaces is considered simultaneously for cluster generation and merging. Membership functions match closely with and describe properly the real distribution of the training data points. Redundant clusters are combined, and the sensitivity to the input order of training data is reduced. Besides, generation of the whole set of clusters from the scratch can be avoided when new training data are considered.

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Determining the number of clusters, which is usually approved by domain experts or evaluated by clustering validity indexes, is an important issue in clustering analysis. This study discusses on the effectiveness of clustering validity indexes for centroid-based partitional clustering algorithms. Most general-purpose clustering validity indexes take the minimum/maximum distance between a pair of data objects, a pair of cluster centroids, or an object and a centroid as an important evaluation factor; however, they may present unstable results especially when two centroids are allocated closely. To alleviate this problem, a new clustering validity index, termed as WLI, is proposed in this paper. Our proposed WLI partially allows, to some extent, the existence of closely allocated centroids in the clustering results by considering not only the minimum but also the median distances between a pair of centroids and therefore possesses the better stability. The performances of WLI and some existing clustering validity indexes are evaluated and compared by running the fuzzy c-means algorithm for clustering various types of data sets, including artificial data sets, UCI data sets, and images. Experimental results have shown that WLI has the more accurate and satisfactory performance than other indexes.

The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients.