Automated Apnoea Detection in Preterms on Non-invasive Ventilation

August 25, 2026 updated by: King's College Hospital NHS Trust

Prospective Observational Study of Automated Apnoea Detection in Preterm Infants Receiving Non-invasive Respiratory Support

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

When the clinical team identifies an infant is eligible for enrolment to the study and following verbal assent of the attending neonatal consultant, a member of clinical staff will initially approach the parents/ legal guardians of eligible infants and if they agree, a researcher. The parents will be provided with an information sheet about the study. The researchers will answer questions and respond to any concerns in a face-to-face meeting. Written informed consent will be obtained.

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 between the exit of the SLE6000/ SLE6000N ventilators (Inspiration Healthcare, Croydon, UK) and the tubing that is attached to the patient. This will ensure pressure and flow data are recorded simultaneously with the sEMG signal. These data will also be fed to the SERA measuring device.

An SpO2 cable (SLE uSpO2, Inspiration Healthcare, Croydon, UK) will be connected to the patient and the ventilator for continuous (second by second) recording of SpO2 levels. These data will be time synced using post processing.

Participants will also be connected to the standard bedside monitor (Phillips Intellivue MX750) for the whole duration of the study.

Surface EMG, pressure, flow and SpO2 data will be synchronised. Two researchers will identify all apnoeic episodes and classify them as central, obstructive or noise.

The ML algorithm will also be used to identify and classify apnoeic episodes that occurred during the study duration.

Comparisons will be made between the apnoeic episodes identified the researchers, the ML algorithm and the nursing staff electronic patient records. These records include classification of an episode as apnoea or desaturation or bradycardia, its duration and any actions taken.

Study Type

Observational

Enrollment (Estimated)

30

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Infants admitted and cared for at the Neonatal Intensive Care Unit at King's College Hospital

Description

Inclusion Criteria:

Preterm infants <32 weeks of gestation at birth and up to 36 weeks postmenstrual age, on non-invasive respiratory support including:

  • non-invasive positive pressure ventilation (NIPPV)
  • nasal continuous positive airway pressure (CPAP)
  • heated humidified high flow nasal cannula (HHFNC) oxygen, either as primary or post extubation respiratory support.

Exclusion Criteria:

  • Infants born above 32 weeks of gestation.
  • Infants with known major congenital abnormalities.
  • Infants above 36 weeks postmenstrual age (PMA).
  • Non-English speakers.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Number of apnoeic episodes correctly identified by the automated machine learning model
Time Frame: From enrollment to the end of monitoring at eight hours
From enrollment to the end of monitoring at eight hours

Secondary Outcome Measures

Outcome Measure
Time Frame
The proportion of apnoeas correctly classified as central or obstructive by the automated machine learning model
Time Frame: From enrollment to the end of monitoring at eight hours
From enrollment to the end of monitoring at eight hours

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

King's College Hospital NHS Trust

Investigators

  • Principal Investigator: Anne Greenough, Professor, King's College Hospital NHS Trust

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

September 7, 2026

Primary Completion (Estimated)

July 27, 2027

Study Completion (Estimated)

July 27, 2027

Study Registration Dates

First Submitted

August 25, 2026

First Submitted That Met QC Criteria

August 25, 2026

First Posted (Actual)

August 31, 2026

Study Record Updates

Last Update Posted (Actual)

August 31, 2026

Last Update Submitted That Met QC Criteria

August 25, 2026

Last Verified

August 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 371040

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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