An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal

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This paper presents efficient low-power adaptive filter architecture for electroencephalogram (EEG) signal and epileptic seizure detection using recurrence quantification analysis (RQA). The preprocessing of EEG is done using notch, wavelet and adaptive filter. The comparison of signal-to-noise ratio of the filter outputs proves that the adaptive filter provides better performance and a parallel interleaved sample of direct form adaptive FIR filter architecture is implemented and low-power issues are addressed. An innovative compressor-based addition technique is utilized in the filter implementation to reduce the area and power consumption. The design is developed using Verilog HDL and mapped to 65-nm technological node. The power results are compared with conventional architecture of adaptive filter. The major advantage of choosing RQA is that it provides better information even for short non-stationary and nonlinear signals where other methods fail to provide good results. And it requires no conventions about data set size or dispersal of the data. The algorithm is applied on epileptic EEG signal from CHB-MIT database. The RQA measures are determined from the recurrence plot, its performance is measured in terms of sensitivity and specificity as 97.4 and 93.5 %, respectively, and leakage power is reduced to 10 %.