# A Study of EEG Feature Complexity in Epileptic Seizure Prediction
###### Abstract
The study reports low sensitivity and high false alarm rates compared to studies that do not use statistical validation and which, instead, report generally optimistic results. Other methods [10, 11, 12] have resorted to simulated pre-ictal data, referred to as surrogate data, generated by a Monte Carlo scheme, for instance, for a classifier-independent means of evaluating a classifier running on a set of given features: If its performance is better on the training data than on the surrogate data, the method is taken to be sound, or is worthy of further investigation. However, no general conclusions are drawn regarding the investigated algorithm seizure prediction ability. The study of Reference [13] investigated a patient-specific monitoring  
Figure 1: The seizure phases in 5 EEG channels, including interictal, pre-ictal, ictal, and post-ictal. system trained from long-term EEG records, and in which seizure prediction combines decision trees and nearest-neighbors classification. Along this vein, Reference [14] used a support vector machine to classify patient-dependent, hand-crafted features. Experiments reported indicate the method decisions have high sensitivity and false alarm rates. Deep learning networks have also served seizure prediction, and studies mention that they can achieve a good compromise between sensitivity and false alarm rates [15; 16]. None of the studies we have reviewed inquired into EEG feature classification complexity, in spite of the importance of this inquiry. As we have indicated earlier, a study of feature classification complexity is essential because it can inform on important properties of the data representation features, such as their mutual discriminant capability and extent, and their relative discriminant efficiency. This, in turn, can benefit feature selection and classifier design. Complexity is generally mentioned by clinicians, who acknowledge, often informally, that common subject-specific EEG features can be highly variable, and that cross-subject features are generally significantly more so. This explains in part why research has so far concentrated on subject-specific data features analysis, rather than cross-subject. Complexity analysis has the added advantage of applying equally to both subject-specific and cross-subject features, thus offering an opportunity to draw beforehand some insight on cross-subject data. The purpose of this study was to investigate the complexity of pre-ictal and inter-ictal classification using features extracted from EEG records, to explore the predictive potential of cross-subject classifiers for epileptic seizure prediction, and evaluate the between-subject variability of the considered features. Using complexity metrics which correlate linearly to classification error [17; 18; 19; 20], this study provides an algorithm-independent cross-subject classification complexity analysis of a set of 88 prevailing features, collected from EEG data of 24 patients. We employed such complexity measures for two-class problems to examine the individual feature ability to distinguish the inter-ictal and pre-ictal periods and to inspect the difficulty of the classification problem. The same complexity metrics generalized to multiple classes are also utilized in order to highlight the variability between patients considering the extracted features. The remainder of this paper gives the details of the data, its processing, and analysis. It is organized as follows: Section 2 describes the materials and methods: the database, the features, the complexity metrics, and the statistical analysis. Section 3 presents experimental results, and Section 4 concludes the paper.