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Related Experiment Video

Updated: Nov 26, 2025

How to Obtain Reliable Visual Event-related Potentials in Newborns

How to Obtain Reliable Visual Event-related Potentials in Newborns

Published on: October 24, 2019

Computational analysis of neonatal ventilator waveforms and loops.

David Chong^1,2, Colin J Morley¹, Gusztav Belteki³

¹Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.

Pediatric Research

|December 8, 2020

Summary

A new algorithm, Ventiliser, automatically analyzes neonatal ventilator data to identify and characterize breathing patterns. This tool accurately recognizes over 97% of ventilator inflations, aiding in the study of mechanical ventilation in infants.

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Area of Science:

Biomedical Engineering
Computational Biology
Neonatology

Background:

Modern neonatal ventilators generate high-frequency data.
Automatic analysis of this data is needed to understand infant ventilation.

Purpose of the Study:

To develop an algorithm for automatic recognition and characterization of ventilator inflations from pressure and flow data.
To create an open-source software package for analyzing neonatal mechanical ventilation.

Main Methods:

Downloaded 100 Hz airway pressure and flow data from Dräger Babylog VN500 ventilators.
Developed the open-source Python package 'Ventiliser' with a rule-based algorithm.
Utilized information gain to identify inflation phases and sub-phases.

Main Results:

Ventiliser analyzes 24 hours of data in 2 minutes on a personal computer.
Achieved >97% accuracy in identifying ventilator inflations and sub-phases in out-of-sample validation.
Generates tables of breath indices and allows waveform visualization.

Conclusions:

Ventiliser provides a computational method for analyzing neonatal ventilator data.
Enables long-term analysis of ventilation patterns and patient-ventilator interactions.
The freely available software supports clinical and research applications.