Mechanical ventilation man-machine asynchronous detection method and device based on graph neural network

Abstract

The invention discloses a mechanical ventilation man-machine asynchronous detection method and device based on a graph neural network, a storage medium and equipment. The detection method comprises the following steps: acquiring real-time waveform data of a to-be-detected object in a mechanical ventilation process; converting the obtained real-time waveform data into real-time graph structure data; and inputting the real-time graph structure data into a pre-trained graph convolutional neural network model to obtain a man-machine asynchronous type. According to the detection method, the waveform data is converted into the graph structure data in the non-Euclidean space, and the graph convolutional neural network model is adopted to predict the man-machine asynchronous type, so that the data dimension is improved, the data analysis freedom degree is enhanced, and the visual sense of analysis processing is widened; therefore, the man-machine asynchronous type prediction accuracy of the to-be-tested object in the mechanical ventilation process is improved.

Description

technical field [0001] The invention belongs to the technical field of physiological signal processing, and in particular relates to a human-machine asynchronous detection method for mechanical ventilation based on a graph neural network, a detection device, a computer-readable storage medium and computer equipment. Background technique [0002] The ventilator has become an important life support device due to its built-in mechanical ventilation function. It uses mechanical ventilation to support people who need breathing assistance under various factors, and has a wide range of applications. [0003] In the process of mechanical ventilation, the timing of mechanical ventilation often does not match the breathing needs of the patient due to external environmental factors with the patient or the ventilator as the carrier. Human-machine asynchronous phenomenon. These human-machine asynchronous phenomena are compared with humans in three observational dimensions: the pressure ...

AI Technical Summary

Problems solved by technology

The waveform data analyzed by these methods is relatively regular, and the degree of freedom is not high; the dimension of the constructed model processing data is low, and the processing vision is relatively narrow

Specifically, the current human-machine asynchronous detection method is relatively simple, mostly based on simple transformation of the initial mechanical ventilation waveform data, and then using the asynchronous information contained in a certain group of mechanical ventilation waveform data segments in the Euclidean space Some constructed algorithmic models for analysis

This process of feature extraction and transformation of the initial waveform data results in a relatively low analysis dimension due to the fact that the original data structure is based on Euclidean space; in addition, the analysis of the original waveform data, which has a weak degree of freedom (the data is relatively regular), is also difficult. Problems that tend to lead to narrower vision for analytical processing

Images