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Automated Apnoea Detection in Preterms on Non-invasive Ventilation

NCT07794007 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 30

Last updated 2026-08-31

No results posted yet for this study

Summary

The aim of this study is to monitor the frequency of apnoeas (pauses in breathing) on various methods of non-invasive respiratory support that are detected by an automated machine-learning (ML) model based on diaphragmatic electromyography (dEMG), in infants born at less than 32 weeks of gestation.

Our hypothesis is that the ML algorithm will improve identification of apnoeic episodes and their classification to central or obstructive.

The study will measure outcomes including the number of apnoeic episodes during the monitoring period, their classification to central and obstructive apnoeas and the predictive ability of the machine-learning algorithm to correctly identify and classify these episodes compared to those documented in nursing charts. Correct classification of apnoeic episodes may help identify underlying causes that require specific intervention.

Conditions

  • Apnoea
  • Prematurity

Interventions

DIAGNOSTIC_TEST

Diaphragmatic electromyography

Electrical activity of the diaphragm, airway pressure, flow and peripheral oxygen saturation levels will be recorded for a duration of eight hours. Transcutaneous diaphragm EMG (sEMG) will be monitored using three surface electrodes (3M Red Dot Foam monitoring electrode 2228, 3M, United Kingdom) that are placed on the infant's abdomen and sternum. The electrodes are connected to a small battery-operated measuring device (SERA, DEMCON; Makawi Medical Systems, the Netherlands) that amplifies and pre-processes the signals received from the electrodes. The pre-processed signals are sent via a Bluetooth connection to a receiving unit that performs higher level processing to derive the EMG signal and other measurements. These results are communicated via a wired connection to a bedside computer running SERA Graphical User Interface (GUI) software. Airway pressure and flow signals will be measured by a flow sensor and pressure tube (Sensirion AG, Stäfa, Switzerland) that will be placed betwe

Sponsors & Collaborators

  • King's College Hospital NHS Trust

    lead OTHER

Principal Investigators

  • Anne Greenough, Professor · King's College Hospital NHS Trust

Eligibility

Max Age
36 Weeks
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-09-07
Primary Completion
2027-07-27
Completion
2027-07-27

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07794007 on ClinicalTrials.gov