Method and device for detecting man-machine asynchronization of mechanical ventilation patient

A technology of mechanical ventilation and synchronous detection, which is applied in the fields of medical health and smart medical care, and can solve problems such as prolonged duration of mechanical ventilation, ventilator damage, and dyspnea

Pending Publication Date: 2022-01-21
北京富通东方科技有限公司
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This adverse PVA affects up to 43% of mechanically ventilated patients and contributes to adverse ev

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method and device for detecting man-machine asynchronization of mechanical ventilation patient
  • Method and device for detecting man-machine asynchronization of mechanical ventilation patient
  • Method and device for detecting man-machine asynchronization of mechanical ventilation patient

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Please refer to figure 1 , the embodiment of the present application provides a method for detecting human-machine asynchronousness in mechanically ventilated patients, which includes:

[0047] S101: Collect respiratory data of a mechanically ventilated patient, the respiratory data including respiratory flow rate data and upper airway pressure data;

[0048] S102: Construct a respiratory data set of mechanically ventilated patients;

[0049] S103: Construct a multi-layer convolutional neural network model with a two-dimensional convolutional neural network as the core;

[0050] S104: Input the data in the respiratory data set of the mechanically ventilated patient into the convolutional neural network model;

[0051] S105: Output the probability value that the mechanically ventilated patient is normal and / or man-machine is out of sync.

[0052] The human-machine out-of-synchronization detection method for mechanically ventilated patients can be applied to smart elec...

Embodiment 2

[0075] Please refer to figure 2 , based on the method of the first embodiment above, the present invention also provides a device for detecting human-machine asynchrony in mechanically ventilated patients, including:

[0076] The data acquisition module is used to collect respiratory data of mechanically ventilated patients, and the respiratory data includes respiratory flow rate data and upper airway pressure data;

[0077] Dataset building blocks for constructing mechanically ventilated patient breath datasets;

[0078] The model building block is used to construct a multi-layer convolutional neural network model with a two-dimensional convolutional neural network as the core;

[0079] an input module, configured to input data in the mechanically ventilated patient respiratory data set into the convolutional neural network model;

[0080] An output module, configured to output the probability value of normal and / or human-machine asynchrony of the mechanically ventilated p...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method and a device for detecting man-machine asynchronization of a mechanical ventilation patient. The method comprises the following steps of: acquiring breathing data of the mechanical ventilation patient, wherein the breathing data comprises breathing flow rate data and upper airway pressure data; constructing a breathing data set of the mechanical ventilation patient; constructing a multi-layer convolutional neural network model taking a two-dimensional convolutional neural network as a core; inputting the data in the breathing data set of the mechanical ventilation patient into the convolutional neural network model; and outputting a probability value of normality and/or man-machine asynchronization of the mechanical ventilation patient. Through the method, accurate identification of man-machine asynchronization of the mechanical ventilation patient is realized, and real-time support is provided for a clinician to adjust the decision of respirator setting.

Description

technical field [0001] The invention relates to the technical fields of smart medical care and medical health, in particular to a method and device for detecting human-machine asynchronousness of a mechanically ventilated patient. Background technique [0002] Respiratory diseases have become one of the deadliest diseases in the world. In order to provide people with the necessary life support, the application and research of mechanical ventilation (Mechanical Ventilation, MV) in clinical treatment has attracted more and more attention from experts. Mechanical ventilation is one of the most important life support treatments for patients who cannot breathe spontaneously. During mechanical ventilation, Patient-Ventilator Asynchrony occurs when the phase of breathing delivered by the ventilator does not match the phase of the patient's respiratory output, or when the patient's needs do not match the assistance the ventilator provides. PVA). PVA is a common condition experienc...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): A61B5/08A61B5/00G06F16/901G06F16/906G06N3/04G06N3/08
CPCA61B5/08A61B5/7267G06F16/901G06F16/906G06N3/08G06N3/047G06N3/045
Inventor 李瑞瑞宁泽惺赵伟
Owner 北京富通东方科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products