Extension-Enhanced Wavelet Decomposition: a Noise and Background Resilient Square-Wave Voltammogram Signal-Processing Technique for Electrochemical Aptamer-Based Biosensing <i>In Vivo</i>

Electrochemical aptamer-based (E-AB) sensors undergo structure-switching upon target binding, making them well-suited for <i>in vivo</i> continuous monitoring of biomolecules with high sensitivity and selectivity. Although square-wave voltammetry (SWV) is the most widely used analytical technique for probing the states of E-AB sensors, precise signal extraction from SWVs acquired during <i>in vivo</i> measurements remains challenging. The difficulty arises due to additive electronic and chemical noise, as well as varying background currents caused by factors such as the reduction of dissolved oxygen, degradation of self-assembly monolayer on the electrodes, biofouling, and other unforeseen effects. Conventional signal extraction algorithms, which typically assume a constant or a linearly varying background current with respect to the scanning potentials, are therefore error prone. In this work, we present a signal-processing technique termed Extension-enhanced Wavelet Decomposition (EWD) that enables background-resilient and noise-reduced SWV peak extraction while preserving quantitative redox signals. Inspired by the symmetric extension technique used in MRI image processing, EWD introduces pseudo-periodicity to the background signals and improves its spectral separation with redox signals from the process of wavelet transformation. We first validate the proposed EWD using simulated data, followed by its application to the datasets from both <i>in vitro</i> and <i>in vivo</i> experiments using several E-AB sensors. Compared to the conventional SWV signal extraction workflow, EWD demonstrates reduced background susceptibility and achieves 1.75 ∼ 3.6-fold improvement in extraction variations from five <i>in vivo</i> datasets measured in whole blood when comparing with conventional SWV signal extraction method.