Overnight In-ear EEG sleep studies

  • Research type

    Research Study

  • Full title

    The efficacy of an in-ear electroencephalography (EEG) sensor to monitor for sleep, compared to scalp EEG, in patients with obstructive sleep apnoea and healthy volunteers.

  • IRAS ID

    211610

  • Contact name

    Mary Morrell

  • Contact email

    m.morrell@imperial.ac.uk

  • Sponsor organisation

    Imperial College London

  • Clinicaltrials.gov Identifier

    N/A, N/A

  • Duration of Study in the UK

    2 years, 8 months, 14 days

  • Research summary

    It is estimated that 75% of patients with sleep disorders are still underdiagnosed. One reason for this under diagnosis is a lack of accurate, simple, cheap device that can measure sleep. Sleep currently measure by Electroencephalography (EEG). EEG consists of several electrodes being placed in specific places over the scalp to detect the brain wave activity. These places are known as the 10-20 system. Based on the frequency and amplitude of the recorded waves, different sleep stages can be identified. The current practice of sleep medicine and diagnosing sleep disorders is mostly based on EEG, and it is also usually associated with 8 cardio-respiratory sensors for the diagnosis of sleep-related breathing disorders. To date, scalp EEG is the only available objective measure of sleep.
    However, patients find the scalp EEG uncomfortable to the extent that it may disturb sleep and it is also time-consuming and expensive compared to portable monitoring such as pulse oximetry. In the UK, the average cost for the in-laboratory recording for overnight sleep study is around six-hundred pounds (not including the follow-up visit).
    Furthermore, there is a need to measure sleep for an extended period of time in some vulnerable populations like older people, and in high-risk occupations such as security worker and truck drivers. Measuring sleep for an extended period of time could predict performance decrements known to be problematic in these populations. In the US, 20% of truck crashes are due to sleep or drowsy driving, and this lead to around 8,952 deaths and 220,000 serious injuries between 2004 and 2013. A simple device that can objectively measure sleep for longer periods could help to overcome these issues.
    An in-ear sleep monitor has been developed by Professor Mandic in the Department of Electrical and Electronic Engineering at Imperial College in collaboration with Professor Morrell. The system uses breakthrough technology to enable continuous measurement of EEG. Electrodes are embedded on an earpiece of viscoelastic material. The idea is to measure in-ear EEG in a wearable sensor. To be acceptable for a long term wear, the sensor should be cheap, user-friendly, robust, unobtrusive, and discreet.
    The ear piece has been built from a commercially available ear plugs with two key components making this sensor easy to use and cost effective. These two features are the memory foam and cloth conductive electrode. The memory foam is a critical characteristic that enables the sensor to be fitted inside the ear securely and safely. The device needs to be squeezed from all sides to make insertion into the ear easy and painless. Then, the sensor distributes and expands evenly following insertion. The memory foam material allows the sensor to absorb the motion and accordingly minimizes the movement artefacts that are usually seen in the scalp EEG. The conductive clothe is flexible and soft to make the sensor comfortable to wear. A small amount of gel is required to achieve low impedance.
    A previous unpublished pilot study has shown that the accuracy, sensitivity, and specificity of the in-ear EEG in detecting slow wave sleep was 0.83, 0.88 and 0.78 respectively.
    The aim of this study is to test this device rigorously in a wide range of participants, healthy volunteers and patients with suspension of Obstructive Sleep Apnoea (OSA).

  • REC name

    North East - Newcastle & North Tyneside 2 Research Ethics Committee

  • REC reference

    17/NE/0023

  • Date of REC Opinion

    30 Jan 2017

  • REC opinion

    Unfavourable Opinion