Wearable In-Ear Encephalography Sensor for Monitoring Sleep. Preliminary Observations from Nap Studies

Looney, David; Goverdovsky,; Rosenzweig, Ivana; Morrell, MJ; Mandic, Danilo P.

doi:10.1513/annalsats.201605-342bc

Cited by 67 publications

(67 citation statements)

References 16 publications

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“…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). These studies all showed promising results, but involved a limited number of participants and also restricted electrode positioning.…”

Section: Introductionmentioning

confidence: 99%

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

Mikkelsen

Ebajemito

Bonmatí-Carrión

et al. 2018

Journal of Sleep Research

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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.

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Section: Introductionmentioning

confidence: 99%

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

Mikkelsen

Ebajemito

Bonmatí-Carrión

et al. 2018

Journal of Sleep Research

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“…Classification performance was evaluated for both scalp-EEG and ear-EEG patterns, and showed that ear-EEG is similarly informative to scalp-EEG to predict sleep stages, which were labelled from a manually scored hypnogram from conventional PSG recording. With a different perspective, our recent study [11] performed simultaneous sleep monitoring from four subjects, using both scalp- and ear-EEG data channels, and reported Substantial Agreement between the corresponding hypnograms, manually and blindly scored by a trained clinician, as shown in Figure 2A. The in-ear EEG data were recorded from our novel ‘one-fits-all’ generic viscoelastic earpieces [16].…”

Section: Introductionmentioning

confidence: 99%

“…For rigour, the ear-EEG automatic scoring procedures were validated for the following scenarios:

Agreement between automatically predicted sleep stages based on ear-EEG patterns and the manually scored hypnogram from ear-EEG ( Scenario 1 ).

Agreement between automatically predicted sleep stages based on ear-EEG patterns and the manually scored hypnogram from scalp-EEG ( Scenario 2 ).

Figure 2B illustrates the proposed analysis framework. The results are benchmarked against the results in [11] where both the scalp- and ear-EEG hypnograms were scored manually. In this way, we establish a proof-of-concept for the feasibility of ear-EEG in automatic scoring of sleep patterns out-of-clinic and in the community.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Sleep Monitoring Using Ear-EEG

Nakamura

Goverdovsky

Morrell

et al. 2017

IEEE J. Transl. Eng. Health Med.

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The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings; and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate substantial to almost perfect agreement, while for Scenario 2 the range of 0.65–0.80 indicates substantial agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.

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“… Wearable technologies to diagnose obstructive sleep apnoea (OSA) : Wearable technologies are increasingly being used to monitor various aspects of health, including sleep . Validation studies on wrist‐worn devices suggest that we are rapidly moving towards devices that can reliably monitor and stage sleep .…”

Section: New Diagnosticsmentioning

confidence: 99%

Update in respiratory sleep disorders: Epilogue to a modern review series

2017

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Wearable In-Ear Encephalography Sensor for Monitoring Sleep. Preliminary Observations from Nap Studies

Abstract: Substantial agreement was observed between recordings derived from a new ear-EEG sensor and conventional scalp electrodes on four healthy volunteers during daytime naps.

Cited by 67 publications

References 16 publications

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

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

Automatic Sleep Monitoring Using Ear-EEG

Update in respiratory sleep disorders: Epilogue to a modern review series

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