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

TL;DR

DeepSleepNet-Lite is a simplified, lightweight sleep scoring model that processes short EEG segments and uses uncertainty estimates via Monte Carlo dropout to improve accuracy and enable real-time sleep analysis.

Contribution

The paper introduces DeepSleepNet-Lite, a novel lightweight architecture for sleep scoring that incorporates uncertainty estimation, reducing computational demands while maintaining competitive performance.

Findings

Achieves comparable accuracy to state-of-the-art models.

Monte Carlo dropout effectively estimates uncertain predictions.

Rejecting uncertain instances improves overall performance.

Abstract

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.