  Deep learning is widely used in the most recent automatic sleep scoring
algorithms. Its popularity stems from its excellent performance and from its
ability to directly process raw signals and to learn feature from the data.
Most of the existing scoring algorithms exploit very computationally demanding
architectures, due to their high number of training parameters, and process
lengthy time sequences in input (up to 12 minutes). Only few of these
architectures provide an estimate of the model uncertainty. In this study we
propose DeepSleepNet-Lite, a simplified and lightweight scoring architecture,
processing only 90-seconds EEG input sequences. We exploit, for the first time
in sleep scoring, the Monte Carlo dropout technique to enhance the performance
of the architecture and to also detect the uncertain instances. The evaluation
is performed on a single-channel EEG Fpz-Cz from the open source Sleep-EDF
expanded database. DeepSleepNet-Lite achieves slightly lower performance, if
not on par, compared to the existing state-of-the-art architectures, in overall
accuracy, macro F1-score and Cohen's kappa (on Sleep-EDF v1-2013 +/-30mins:
84.0%, 78.0%, 0.78; on Sleep-EDF v2-2018 +/-30mins: 80.3%, 75.2%, 0.73). Monte
Carlo dropout enables the estimate of the uncertain predictions. By rejecting
the uncertain instances, the model achieves higher performance on both versions
of the database (on Sleep-EDF v1-2013 +/-30mins: 86.1.0%, 79.6%, 0.81; on
Sleep-EDF v2-2018 +/-30mins: 82.3%, 76.7%, 0.76). Our lighter sleep scoring
approach paves the way to the application of scoring algorithms for sleep
analysis in real-time.
