Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection

Abstract

An automatic detection system for distinguishing healthy, ictal, and inter-ictal EEG signals plays an important role in medical practice. This paper presents a very large scale integration (VLSI) architecture of three-class classification for epilepsy and seizure detection. In order to find out the most efficient three-class classification scheme for hardware implementation, several multiclass non-linear support vector machine (NLSVM) classifiers are compared and validated using software implementation. Finally, the one-against-one (OAO) multiclass NLSVM is selected due to its highest accuracy. The designed system consists of a discrete wavelet transform (DWT)-based feature extraction module, a modified sequential minimal optimization (MSMO) training module, and an OAO multiclass classification module. A lifting structure of Daubechies order 4 wavelet is introduced in three-level DWT to save circuit area and speed up the computational time. The MSMO is used for on-chip training. The circuit of the largest absolute value decision is designed to avoid the unclassifiable problem in the OAO multiclass classification. The designed system is implemented on a field-programmable gate array (FPGA) platform and evaluated using the publicly available epilepsy dataset. The experimental results demonstrate that the designed system achieves high accuracy with low-dimensional feature vectors.