Fast automated on-chip artefact removal of EEG for seizure detection based on ICA-R algorithm and wavelet denoising

Portable automatic seizure detection systems can greatly improve the quality of life of epileptic patients. To improve the performance of seizure detection, independent component analysis (ICA) is implemented in these systems to extract artefacts of electroencephalogram (EEG), and then wavelet denoising method is used to remove the artefacts. However, classical ICA requires post-identification of the components containing artefacts, which cause inefficiency. In this study, integrated circuit implementation of fast ICA with reference algorithm and wavelet denoising method is carried out to enable on-chip artefact extraction and removal without post-identification. This system consists of extraction and removal module, which are designed highly parallel to speed up computation, and therefore, save time for seizure detection. The presented system is verified on Kintex-7 field-programmable gate array using synthesised signal and real EEG data. Experiment results show that the designed system is fully functional and speeds up the artefact removal process.