Four Level Haar Transform Architecture for Feature Extraction

Biomedical Electronic Devices have been developed as aid in some neurological conditions like epilepsy and Parkinson. Those devices require low-power systems in order to guarantee portability and continuous operation from weeks to years. Therefore, the present work proposes a simplified architecture for Haar Wavelet Transform used as feature extraction in the spike sorting process. The architecture achieves a reduction of 46% on the number of multipliers needed in a direct architecture. As result of the reduction onmultiplication, the rounding off error in the proposed architecture is also reduced achieving zero error in spike sorting classification.