  Temporary changes in electrical resistance of a nanopore sensor caused by
translocating target analytes are recorded as a sequence of pulses on current
traces. Prevalent algorithms for feature extraction in pulse-like signals lack
objectivity because empirical amplitude thresholds are user-defined to single
out the pulses from the noisy background. Here, we use deep learning for
feature extraction based on a bi-path network (B-Net). After training, the
B-Net acquires the prototypical pulses and the ability of both pulse
recognition and feature extraction without a priori assigned parameters. The
B-Net performance is evaluated on generated datasets and further applied to
experimental data of DNA and protein translocation. The B-Net results show
remarkably small relative errors and stable trends. The B-Net is further shown
capable of processing data with a signal-to-noise ratio equal to one, an
impossibility for threshold-based algorithms. The developed B-Net is generic
for pulse-like signals beyond pulsed nanopore currents.
