Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines

In this paper, we investigate the potentials of applying a kernel-based learning machine, the Relevance Vector Machine (RVM), to the task of epilepsy detection through automatic electroencephalogram (EEG) signal classification. For this purpose, some experiments have been conducted over publicly available data, contrasting the performance levels exhibited by RVM models with those achieved with Support Vector Machines (SVMs), both in terms of predictive accuracy and sensitivity to the choice of the kernel function. Four settings of both types of kernel machine were considered in this study, which vary in accord with the type of input data they receive, either raw EEG signal or some statistical features extracted from the wavelet-transformed data. The empirical results indicate that: (1) in terms of accuracy, the best-calibrated RVM models have shown very satisfactory performance levels, which are rather comparable to those of SVMs; (2) an increase of accuracy is sometimes accompanied by loss of sparseness in the resulting RVM models; (3) both types of machines present similar sensitivity profiles to the kernel functions considered, having some kernel parameter values clearly associated with better accuracy rate; (4) when not making use of a feature extraction technique, the choice of the kernel function seems to be very relevant for significantly leveraging the performance of RVMs; and (5) when making use of derived features, the choice of the feature extraction technique seems to be an important factor to one take into account.