CPR Ventilator Airway Pressure Feedback for Ventilation Timing

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Solution Overview

Problem

Existing cardiopulmonary resuscitation methods face challenges in automatically coordinating compressions and ventilation, leading to difficulties in precise and stable control over extended periods, reducing practicality due to reliance on manual coordination or communication protocols.

Innovation Solution

A ventilator system that utilizes airway pressure feedback and machine learning to automatically determine ventilation timing based on individual body parameters, compression frequency, and phase, enabling precise and stable ventilation control through a machine learning model trained with preset parameters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual coordination methods are used to manage CPR compressions and ventilation, then medical staff can control the process, but precise and stable control for long periods becomes difficult and reduces practicality

Engineering Contradiction:
Improveventilation timing precisionVSAvoidcontrol stability
Core Design Contradiction:
Measurement precisionVSReliability

Solution Approach 1:

The ventilator system automatically detects compression phase and frequency, then autonomously determines and executes ventilation timing without continuous human intervention. The system serves itself by using its own sensors and processors to monitor CPR parameters and trigger ventilation automatically, eliminating the need for manual coordination while maintaining precise and stable control over extended periods.

Inventive Principle:
Principle #25Self-service

2Ease of operation

If communication protocols between compression equipment and ventilators are used, then coordination between devices is achieved, but automatic coordination capability is lost and manual management is required

Engineering Contradiction:
Improvecoordination automationVSAvoidsystem integration
Core Design Contradiction:
Ease of operationVSDevice complexity

Solution Approach 1:

The ventilator system integrates multiple functions into a single unified device: it combines compression detection capabilities, phase analysis algorithms, and ventilation control mechanisms. By merging these previously separate functions (compression monitoring, timing calculation, and ventilation execution) into one integrated system, the invention achieves automatic coordination without requiring complex communication protocols between multiple devices.

Inventive Principle:
Principle #5Merging (Combining)

3Stability of the object's composition

If mechanical compression equipment is used instead of manual compressions, then consistent compression delivery is improved, but coordination with ventilation becomes more complex

Engineering Contradiction:
Improvecompression consistencyVSAvoidcoordination mechanism
Core Design Contradiction:
Stability of the object's compositionVSDevice complexity

Solution Approach 1:

The ventilator system continuously monitors compression parameters (phase and frequency) through integrated sensors and uses this real-time feedback to automatically adjust ventilation timing. The system processes compression signals, identifies compression phase and frequency dynamically, and uses this feedback information to trigger ventilation at the optimal moment, achieving seamless coordination between mechanical compression and ventilation without complex external control mechanisms.

Inventive Principle:
Principle #23Feedback

Data Source

PatentEP4545124B1Ventilator configured for implementing a method based on automatic airway pressure feedback for cardiopulmonary resuscitation
Publication Date: 2026.02.04 GUANGZHOU LANDSWICK MEDICAL TECH LTD
  • EP4545124B1 patent drawingFigure 1
  • EP4545124B1 patent drawingFigure 2
  • EP4545124B1 patent drawingFigure 3~4

AI summary

The present disclosure reveals a cardiopulmonary resuscitation airway pressure automatic feedback ventilation method and ventilator, which includes the following steps: obtaining the current airway pressure signal when compressing the heart of the target individual at a preset sampling frequency; dynamically correcting the current airway pressure signal based on the body parameters of the target individual to obtain the target airway pressure signal; continuously analyzing the target airway pressure signal to obtain the compression frequency, compression phase, and high-dimensional signal information for the target individual; training a machine learning model with preset compression parameters and ventilation rule parameters; inputting the compression frequency, compression phase, and high-dimensional signal information for the target individual, along with the mode setting parameters of the ventilation equipment, into the machine learning model to determine the ventilation probability and ventilation time window of the target pressure signal, and controlling the ventilation equipment to perform automatic ventilation on the target individual according to the ventilation time window, thereby improving safety, reliability, and stability.