Human-machine asynchronous mechanical ventilation detection method based on circulating neural network

Abstract

The invention relates to a man-machine asynchronous mechanical ventilation detection method based on a circulating neural network. The method comprises the following steps: a, collecting respiratory records during mechanical ventilation, and marking types of man-machine asynchrony by an expert; b, training and testing a multi-channel circulating neural network model on the basis of the marked respiratory records; c, detecting occurrence of various types of man-machine asynchrony by multi-model integration architecture. Characteristics of respiratory recording pressure waveforms and flow waveforms are fully extracted by adopting the multi-channel model structure, and after the characteristics are fused, the circulating neural network model is used for detecting occurrence of man-machine asynchrony. The occurrence of various types of man-machine asynchrony in the respiratory records can be simultaneously detected.

Description

technical field

[0001] The invention relates to a cyclic neural network technology, in particular to a detection method, which is based on a cyclic neural network model to detect man-machine asynchrony during mechanical ventilation. Background technique

[0002] Mechanical ventilation refers to the use of a ventilator to replace or assist the work of the respiratory muscles when the respiratory organs cannot maintain normal gas exchange, that is, when respiratory failure occurs. Mechanical ventilation strives for treatment time and creates conditions for clinical respiratory failure caused by various reasons, as well as other diseases that require respiratory function support.

[0003] Today, with the popularity of ventilators, many clinicians only focus on their basic functions such as relieving respiratory muscle fatigue, improving ventilation and oxygenation. Although the patient's vital signs are maintained and blood gas indicators are improved, the patient's subjective...

Examples

Embodiment Construction

[0030] In order to better explain the present invention and facilitate understanding, the present invention will be described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0031] refer to Figure 1 ~ Figure 3 , a method for detecting human-computer asynchronous mechanical ventilation based on a recurrent neural network, comprising the following steps:

[0032] aCollect respiratory records during mechanical ventilation, and have the type of man-machine asynchrony marked by experts;

[0033] b Training and testing a multi-channel recurrent neural network model with labeled respiration recordings;

[0034] cUsing a multi-model integration architecture to detect the occurrence of various types of man-machine asynchrony, the process is as follows:

[0035] c1 Simultaneously input respiratory records into flow rate models, trigger models, cycle models and other models, and detect whether there are four types of man-machine asynchrony...

technical field

[0001] The invention relates to a cyclic neural network technology, in particular to a detection method, which is based on a cyclic neural network model to detect man-machine asynchrony during mechanical ventilation. Background technique

[0002] Mechanical ventilation refers to the use of a ventilator to replace or assist the work of the respiratory muscles when the respiratory organs cannot maintain normal gas exchange, that is, when respiratory failure occurs. Mechanical ventilation strives for treatment time and creates conditions for clinical respiratory failure caused by various reasons, as well as other diseases that require respiratory function support.

[0003] Today, with the popularity of ventilators, many clinicians only focus on their basic functions such as relieving respiratory muscle fatigue, improving ventilation and oxygenation. Although the patient's vital signs are maintained and blood gas indicators are improved, the patient's subjective...

Examples

Embodiment Construction

[0030] In order to better explain the present invention and facilitate understanding, the present invention will be described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0031] refer to Figure 1 ~ Figure 3 , a method for detecting human-computer asynchronous mechanical ventilation based on a recurrent neural network, comprising the following steps:

[0032] aCollect respiratory records during mechanical ventilation, and have the type of man-machine asynchrony marked by experts;

[0033] b Training and testing a multi-channel recurrent neural network model with labeled respiration recordings;

[0034] cUsing a multi-model integration architecture to detect the occurrence of various types of man-machine asynchrony, the process is as follows:

[0035] c1 Simultaneously input respiratory records into flow rate models, trigger models, cycle models and other models, and detect whether there are four types of man-machine asynchrony...

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