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A Recurrent Neural Network-Based Detection Method for Man-machine Dyssynchrony in Mechanical Ventilation

A circulating neural network and mechanical ventilation technology, applied in the field of circulating neural network, can solve the problems of prolonging the time of mechanical ventilation, staying in ICU and total hospitalization time, increasing the mortality rate and so on.

Active Publication Date: 2021-04-06
杭州智瑞思科技有限公司
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Problems solved by technology

The literature shows that the dyssynchrony index (number of asynchronous breaths per unit time / (total number of triggered and untriggered breaths)*100%) exceeding 10% will significantly prolong the duration of mechanical ventilation, ICU stay and total hospital stay, and increase the mortality rate

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  • A Recurrent Neural Network-Based Detection Method for Man-machine Dyssynchrony in Mechanical Ventilation
  • A Recurrent Neural Network-Based Detection Method for Man-machine Dyssynchrony in Mechanical Ventilation
  • A Recurrent Neural Network-Based Detection Method for Man-machine Dyssynchrony in Mechanical Ventilation

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[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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Abstract

A method for detecting human-computer asynchrony in mechanical ventilation based on a recurrent neural network, comprising the following steps: a. collecting respiratory records during mechanical ventilation, and marking the type of human-computer asynchrony by experts; The channel recurrent neural network model; c adopts a multi-model integration architecture to detect the occurrence of various types of man-machine asynchrony. The present invention adopts a multi-channel model structure to fully extract the characteristics of the pressure waveform and the flow waveform of the respiratory record, and uses a cyclic neural network model to detect the occurrence of man-machine asynchrony after feature fusion. Using a multi-model integrated architecture, it can simultaneously detect the occurrence of multiple types of human-computer asynchrony in respiratory recordings.

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...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61M16/00
Inventor 毛科栋葛慧青段开亮李冰黄超
Owner 杭州智瑞思科技有限公司
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