Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy

Abstract

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low‐cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex‐printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self‐applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier (“random forests”) and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter‐individual variation in sleep parameters. The results demonstrate that machine‐learning‐based scoring of around‐the‐ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine‐learning‐based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine‐learning‐based scoring holds promise for large‐scale sleep studies.

Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch

“…With the obtained accuracy of 80.8%, single-channel sleep staging with the secondary EOG reaches, and sometimes even outperforms, many deep-learning approaches based on the primary EEG presented in Table I. Therefore, this finding would allow one to explore the usage of EOG as an alternative for EEG in many applications, such as home-based sleep monitoring with wearable devices [6], [24].…”

“…The reason for this is that deep neural networks usually require a large amount of data to train. In practice, many sleep studies possess a small cohort, such as a few dozens of subjects [3], [4], [5] sometimes even less [6], [7], particularly those studies related to sleep disorders [7], [8] or exploring new channel positions [6]. As a consequence, the small amount Corresponding author: h.phan@kent.ac.uk.…”

“…However, these databases cannot be simply added into these studies due to channel mismatch. The problem of channel mismatch arises when different studies uses different channel layouts [2] or when novel electrode placements might be explored [6]. Moreover, it also happens when a study investigates a particular sleep abnormality, poor performance can be obtained when the automated diagnostic procedure is only trained on healthy volunteers [7].…”

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

“…First, as a sequence-to-sequence model, the network needs to access entire sequences of multiple epochs to perform classification. This could delay online and realtime applications, such as sleep monitoring [16], [17]. Second, the class-wise results in Table II show opposite behaviors of SeqSleepNet and Deep-SleepNet on N3 and REM.…”

The future of sleep health: a data-driven revolution in sleep science and medicine

“…Similarly, several miniaturised EEG devices have shown promising results with regards to their ability to classify sleep stages 60,61 . In ear, EEG is a modality that has shown promise in recent years, for instance, Mikkelsen et al 62 compared in-ear mobile EEG analysed through machine learning-based automated scoring to conventional, manual scored PSG and commercial-grade actigraphy showed promising results, although also constrained to a laboratory environment. A 2019 study showed that automatic sleep stage prediction based on a single in-ear sensor demonstrated a 74% agreement with the hypnogram generated from full PSG, which is promising but still requires further work for it to be at a clinical standard of performance (90% agreement) 63 .…”