VLSI wavelet denoising of neural signals: Critical appraisal of different algorithmic solutions for threshold estimation

Wavelet denoising represents a common preprocessing step for several biomedical applications exposing low SNR. When the real-time requirements are joined to the fulfilment of area and power minimization for wearable/ implantable applications, such as for neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithm should be carefully tuned. The usually overlooked part related to threshold estimation is deeply analysed in this paper, in terms of required hardware resources and functionality, exploiting Xilinx System Generator for the design of the architecture and the co-simulation. The analysis reveals how the widely used Median Absolute Deviation (MAD) could lead to hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application.