  Biomedical decision making involves multiple signal processing, either from
different sensors or from different channels. In both cases, information fusion
plays a significant role. A deep learning based electroencephalogram channels'
feature level fusion is carried out in this work for the electroencephalogram
cyclic alternating pattern A phase classification. Channel selection, fusion,
and classification procedures were optimized by two optimization algorithms,
namely, Genetic Algorithm and Particle Swarm Optimization. The developed
methodologies were evaluated by fusing the information from multiple
electroencephalogram channels for patients with nocturnal frontal lobe epilepsy
and patients without any neurological disorder, which was significantly more
challenging when compared to other state of the art works. Results showed that
both optimization algorithms selected a comparable structure with similar
feature level fusion, consisting of three electroencephalogram channels, which
is in line with the CAP protocol to ensure multiple channels' arousals for CAP
detection. Moreover, the two optimized models reached an area under the
receiver operating characteristic curve of 0.82, with average accuracy ranging
from 77% to 79%, a result which is in the upper range of the specialist
agreement. The proposed approach is still in the upper range of the best state
of the art works despite a difficult dataset, and has the advantage of
providing a fully automatic analysis without requiring any manual procedure.
Ultimately, the models revealed to be noise resistant and resilient to multiple
channel loss.
