Automatic detection of microsleep episodes with deep learning

TL;DR

This study demonstrates that deep learning models can reliably detect microsleep episodes from raw EEG and EOG data, approaching expert-level accuracy, which could improve clinical vigilance assessments.

Contribution

The paper introduces a novel deep learning approach for automatic detection of microsleep episodes using raw EEG and EOG data, addressing limitations of traditional scoring methods.

Findings

Deep learning models achieved performance close to human experts.

Detection was highly accurate for wakefulness and microsleep episodes.

Borderline segments like MSEc and ED had lower detection accuracy, similar to expert inter-rater reliability.

Abstract

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30-s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e. with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. Code of algorithms ( https://github.com/alexander-malafeev/microsleep-detection ) and data ( https://zenodo.org/record/3251716 ) are available.