Automatic sleep staging using ear-EEG

Abstract

Background Sleep and sleep quality assessment by means of sleep stage analysis is important for both scientific and clinical applications. Unfortunately, the presently preferred method, polysomnography (PSG), requires considerable expert assistance and significantly affects the sleep of the person under observation. A reliable, accurate and mobile alternative to the PSG would make sleep information much more readily available in a wide range of medical circumstances. New method Using an already proven method, ear-EEG, in which electrodes are placed inside the concha and ear canal, we measure cerebral activity and automatically score the sleep into up to five stages. These results are compared to manual scoring by trained clinicians, based on a simultaneously recorded PSG. Results The correspondence between manually scored sleep, based on the PSG, and the automatic labelling, based on ear-EEG data, was evaluated using Cohen’s kappa coefficient. Kappa values are in the range 0.5–0.8, making ear-EEG relevant for both scientific and clinical applications. Furthermore, a sleep-wake classifier with leave-one-out cross validation yielded specificity of 0.94 and sensitivity of 0.52 for the sleep stage. Comparison with existing method(s) Ear-EEG based scoring has clear advantages when compared to both the PSG and other mobile solutions, such as actigraphs. It is far more mobile, and potentially cheaper than the PSG, and the information on sleep stages is far superior to a wrist-based actigraph, or other devices based solely on body movement. Conclusions This study shows that ear-EEG recordings carry information about sleep stages, and indicates that automatic sleep staging based on ear-EEG can classify sleep stages with a level of accuracy that makes it relevant for both scientific and clinical sleep assessment.

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

“…For automatic sleep scoring, we developed a custom-made sleep scoring algorithm (using a "random forest" classifier as described below) by closely following the feature-based approach proposed in Mikkelsen, Villadsen, et al (2017) (in turn inspired by Koley & Dey, 2012).…”

“…Each tree was trained on a resampling of the original training set with the same number of elements (but duplicates T A B L E 1 An overview of the features used in this study, grouped by type. All features are described in Mikkelsen, Villadsen, et al (2017). The EOG and EMG "proxies" are created by band-pass filtering the cEEGrid data (using 0.…”

“…Recently, significant progress has been made in the field of mobile electroencephalograms (EEGs) (De Vos, Gandras, & Debener, 2014;Debener, Minow, Emkes, Gandras, & de Vos, 2012), indicating that miniaturized EEG systems can be used outside the laboratory environment. An elegant solution to avoid placing electrodes on the head in locations where they are visible or difficult to apply, has been proposed in the form of a miniaturized EEG device placed in or around the ears, offering both a reliable and user-friendly alternative for full-scalp EEG (Mikkelsen, Kappel, Mandic, & Kidmose, 2015;Mikkelsen, Kidmose, & Hansen, 2017;Mikkelsen, Villadsen, Otto, & Kidmose, 2017;Pacharra, Debener, & Wascher, 2017). More specifically, several studies have reported progress towards using such ear-centered EEG devices for tracking the presence of different sleep stages (Looney, Goverdovsky, Rosenzwei, Morrell, & Mandic, 2016;Mikkelsen, Villadsen, et al, 2017;Stochholm, Mikkelsen, & Kidmose, 2016;Zibrandtsen, Kidmose, Otto, Ibsen, & Kjaer, 2016).…”

“…Although there is an extensive literature on automated algorithms (for an up-to-date review see Boostani, Karimzadeh, & Nami, 2017), we have used ensembles of decision trees, so-called random forests, on an extended set of features, as this family of classifiers have been shown to perform particularly well for the task of sleep scoring (Boostani et al, 2017;Fraiwan et al 2012;Mikkelsen, Villadsen, et al, 2017). To investigate the need for specialized algorithms for cEEGrid recordings we compared the performance with a commercial algorithm (packaged with DOMINO by Somnomedics Gmbh) applied to the cEEGrid recordings.…”

Detection of generalized tonic-clonic seizures from ear-EEG based on EMG analysis

“…Ear-EEG a wearable EEG recording method, where electrodes are placed in and around the ear. Ear-EEG is of particular interest for long-term monitoring in epilepsy [11,12] and sleep [13,14]. Ear-EEG has also been used for hearing threshold estimation [15].…”