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Single-molecule sensors based on carbon nanotubes transducer, enable to probe stochastic molecular dynamics thanks to long acquisition periods and high throughput measurements. With such sampling conditions, the sensor baseline may drift significantly and induce fake states and transitions in the recorded signal, leading to wrong kinetic estimates from the inferred state model.

We present MDL-AdaCHIP a multiscale signal compression technique based on the Minimum Description Length (MDL) principle, combined with an Adaptive piecewise Cubic Hermite Interpolation (AdaCHIP), both implemented into a blind source separation framework to compensate the parasitic baseline drift in single-molecule biosensors

This paper considers the detection of possible deviation from a nominal distribution for continuously valued random variables. Specifically, under the null hypothesis, samples are distributed approximately according to a nominal distribution. Any significant departure from this nominal distribution constitutes the alternative hypothesis. It is established that for such deviation detection where the nominal distribution is only specified under the null hypothesis, Kullback-Leibler distance is not a suitable measure for deviation.