DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates

Fiorillo, Luigi; Favaro, Paolo; Faraci, Francesca Dalia (2021). DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates. IEEE transactions on neural systems and rehabilitation engineering, 29, pp. 2076-2085. IEEE 10.1109/TNSRE.2021.3117970

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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 process raw signals
and to learn feature directly 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 realtime.

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