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.

中文翻译：

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基于ICA-R算法和小波去噪的EEG快速自动片上伪像去除，用于癫痫发作检测

便携式自动癫痫发作检测系统可以极大地改善癫痫患者的生活质量。为了提高癫痫发作检测的性能，在这些系统中实施了独立成分分析（ICA）来提取脑电图（EEG）的伪影，然后使用小波去噪方法去除了这些伪影。但是，传统的ICA需要事后识别包含伪影的组件，这会导致效率低下。在这项研究中，采用参考算法和小波去噪方法对快速ICA进行了集成电路实现，以实现片上伪像的提取和去除，而无需后期识别。该系统由提取和去除模块组成，该模块高度并行设计以加快计算速度，因此节省了检出癫痫发作的时间。使用合成信号和实际EEG数据，在Kintex-7现场可编程门阵列上对提出的系统进行了验证。实验结果表明，所设计的系统具有完整的功能，并加快了人工制品的去除过程。