  The search for gravitational-wave signals is limited by non-Gaussian
transient noises that mimic astrophysical signals. Temporal coincidence between
two or more detectors is used to mitigate contamination by these instrumental
glitches. However, when a single detector is in operation, coincidence is
impossible, and other strategies have to be used. We explore the possibility of
using neural network classifiers and present the results obtained with three
types of architectures: convolutional neural network, temporal convolutional
network, and inception time. The last two architectures are specifically
designed to process time-series data. The classifiers are trained on a month of
data from the LIGO Livingston detector during the first observing run (O1) to
identify data segments that include the signature of a binary black hole
merger. Their performances are assessed and compared. We then apply trained
classifiers to the remaining three months of O1 data, focusing specifically on
single-detector times. The most promising candidate from our search is
2016-01-04 12:24:17 UTC. Although we are not able to constrain the significance
of this event to the level conventionally followed in gravitational-wave
searches, we show that the signal is compatible with the merger of two black
holes with masses $m_1 = 50.7^{+10.4}_{-8.9}\,M_{\odot}$ and $m_2 =
24.4^{+20.2}_{-9.3}\,M_{\odot}$ at the luminosity distance of $d_L =
564^{+812}_{-338}\,\mathrm{Mpc}$.
