Efficient Implementation of Robust CUSUM Algorithm to Characterize Nanogaps Measurements with Heavy-Tailed Noise

Detection of bio-molecules through quantum tunneling currents could lead to the next-generation DNA sequencing methods. In order to analyze the stability of these sensitive devices, it is necessary to characterize their conductance switching statistics. This characterization can be realized by denoising the tunneling current signal and clustering the outcomes. The first step can be done with the CUSUM algorithm, which detects abrupt changes and has been used in similar devices. We found heavy-tailed non-Gaussian noise in the measurement setup of the experimental devices. This paper suggests an approximation in the likelihood ratio step of the CUSUM algorithm that is more robust than the simple Gaussian noise assumption and, at the same time, is computationally more efficient than computing the fitted true likelihoods.