Very large scale integration implementation of seizure detection system with on-chip support vector machine classifier

Epilepsy is one of the most common neurological disorders; it affects millions of people globally. Because of the risks to health that it causes, the study and analysis of epilepsy have been given considerable attention in the biomedical field. In a neurological diagnosis, an automated device for detecting seizures or epilepsy from an electroencephalogram (EEG) signal has a significant role. This research work proposes a very large scale integration implementation system for the automatic detection of seizures. Before classification, feature extraction was performed by discrete wavelet transform (DWT) and on-chip classification was performed by a linear support vector machine. The polyphase architecture of Daubechies fourth-order wavelet three-level DWT was used to minimize computational time. The systolic array architecture-based support vector machine classifier using parallel processing helps to minimize the computational complexity of the proposed method. This research work uses an open access EEG dataset. Hardware implementation was done on a field-programmable gate array (FPGA). Efficient results were produced compared with the existing system on chip (SoC) and FPGA seizure detection systems.