A 392-pW 42.7-dB Gm-C wavelet filter for low-frequency feature extraction used for wearable sensor

Continuous wavelet transform (CWT) has been proven to be an effective tool in feature extraction of non-stationary bio-signals. Therefore, hardware implementation of CWT has been widely investigated in wearable sensor integrated with local intelligence algorithm. To realize the feature extraction from low-frequency bio-signals, the design method of low-frequency Gm-C wavelet filter used in wearable sensor has been proposed in this paper. To alleviate the power constraint by wearable device, the Leap-Frog multiple-loop feedback filter structure is employed, which has low circuit complexity and sensitivity. Also, the transconductor consisting of simple differential pair is employed as Gm cell. By using low level bias current and deep weak inversion, low transconductance can be achieved to realize low frequency operation. A sixth-order Gaussian wavelet filter is designed in 0.18 μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}m CMOS process. Simulation results show that power consumption is only 392 pW at center frequency of 5.9 Hz, corresponding to dynamic range of 42.7 dB and figure-of-merit of 2.59 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} 10-13\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{-13}$$\end{document}. Experiment result shows that the proposed wavelet filter can be used for accurate extraction of transient features in biomedical signal processing.