IoT4 HealthySleep - paediatric data

  • Research type

    Research Study

  • Full title

    Internet of Things for Healthy Sleep - using paediatric data to inform machine-learning algorithms

  • IRAS ID

    278091

  • Contact name

    Heather Elphick

  • Contact email

    h.elphick@nhs.net

  • Sponsor organisation

    Sheffield Children's Hospital

  • Duration of Study in the UK

    0 years, 1 months, 9 days

  • Research summary

    Paediatric sleep centres in the UK diagnose and treat children with suspected sleep disorders such as obstructive sleep apnoea, central sleep apnoea and narcolepsy. To investigate and diagnose sleep disorders a range of wired sensors are attached to an infant, child or teenager overnight to monitor their breathing and sleep patterns overnight. Such investigations require a child and carer to be resident in hospital overnight, sleeping in a strange environment with up to 27 wired sensors attached to their head, face and body. In addition, all physiological parameters measured during a sleep study are then manually interpreted by skilled physiologists prior to the clinician making diagnostic and treatment decisions.

    Sheffield Children’s NHS Foundation Trust (SCHFT)
    Sheffield Children’s Hospital Sleep Unit carries out approximately 500 sleep studies a year to investigate sleep-related breathing disorders in children. This provides a potentially rich clinical data set.

    The University of Sheffield & University of Oxford
    The Universities are experts in signal processing and control, and work with different applications of datasets (for example predicting traffic flow from CCTV video recordings; tracking of wildlife through video recordings). Working with clinical sleep data is a new exciting challenge for experts in signal processing.

    This research project aims to bring together computer science academics and sleep health professionals to simplify the whole sleep diagnostic and sleep scoring process. The aim of the current study is to use existing paediatric sleep study data to create automated algorithms to enable data to be reported directly to the clinicians without the need for manual scoring.

  • REC name

    North West - Greater Manchester West Research Ethics Committee

  • REC reference

    20/NW/0070

  • Date of REC Opinion

    6 Apr 2020

  • REC opinion

    Further Information Favourable Opinion