Design of programmable Gaussian‐derived wavelet filter for wearable biomedical sensor

Abstract

To provide multiple options for specific application in biosignal processing, the programmable Gaussian‐derived Gm‐C wavelet filter has been proposed. To realize the programmable characteristic, the analog wavelet base with one numerator term is constructed by using hybrid artificial fish swarm algorithm. Also, the inverse follow‐the‐leader feedback Gm‐C filter structure with a switch array is employed. By programming switches only, Gaussian and Marr wavelet transforms can be realized flexibly with all component parameters unchanged. The seventh‐order programmable wavelet filter is designed as an example. Simulation results show that power consumption is only 141.68 pW at scale a = 0.1, with dynamic range of 42.6 dB and figure‐of‐merit of 2.05 × 10−13. Due to the programmability, the proposed design method can implement two wavelet filters with very low circuit complexity.

Design of a class of fractional-order analog wavelet filters

“…1 They have been widely used in speech signal processing, fault detection, biomedical engineering, and other fields. [2][3][4][5] Among them, the need for ultra-low power biomedical signal analysis represented by the electronic cochlea, implantable cardiac pacemakers, and wearable dynamic electroencephalogram systems has promoted the development of analog wavelet filters. [6][7][8] However, with the continuous expansion of research objects and scopes, the limitations of analog wavelet filters in dealing with some problems are gradually exposed.…”