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A cascaded identification method for sahs screening, breathing mode and sleep state

A sleep state and identification method technology, applied in the evaluation of respiratory organs, medical science, diagnosis, etc., can solve the problems that cannot contain all the information of pulse wave, cannot monitor respiratory events and sleep state, etc., and achieve the effect of reducing physiological load

Active Publication Date: 2021-02-12
SUN YAT SEN UNIV
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AI Technical Summary

Problems solved by technology

There are also machine learning-based methods that use a single pulse or a single blood oxygen saturation signal to monitor sleep-disordered breathing, but they cannot monitor respiratory events and sleep states, and the eigenvalues ​​used cannot contain all the information of the pulse wave

Method used

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  • A cascaded identification method for sahs screening, breathing mode and sleep state
  • A cascaded identification method for sahs screening, breathing mode and sleep state
  • A cascaded identification method for sahs screening, breathing mode and sleep state

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Embodiment 1

[0031] A cascade identification method for SAHS screening, breathing mode and sleep state, comprising the following steps:

[0032] (1) Construct a data set, derive the pulse wave data of patients and normal people from polysomnography (PSG), which is the first type of data set; from the pulse wave data in patients, calibrate different breathing patterns, including normal breathing, obstructive Five types of respiratory events, hypopnea respiratory events, central respiratory events, and mixed respiratory events are the second type of data sets; from the pulse wave data of patients, different sleep states are calibrated, including Weak period, REM period, N1 period, The 5 categories of N2 and N3 are the third data set; from the pulse wave data of normal people, different sleep states are calibrated, which also includes the above 5 categories, which is the fourth data set; the respiratory event classification and sleep state classification are as follows: figure 2 , 3 shown; ...

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Abstract

The present invention provides a cascaded recognition method for SAHS screening, breathing mode and sleep state, the method extracts the time domain, frequency domain and nonlinear features of the pulse wave, and evaluates and scores all the extracted feature values, Screening, pattern recognition with machine learning models such as random forests. Select the optimal feature group to identify SAHS patients and normal people; for the identified SAHS patient group, perform feature evaluation and scoring on the original eigenvalue set, and select the optimal feature group for respiratory mode and sleep state identification; In the normal group, the original feature value set is also evaluated and scored, and the optimal feature set is selected for sleep state recognition. This enables cascading classification. The invention utilizes a single-lead pulse wave signal to carry out SAHS screening, respiratory mode and sleep state identification, greatly reducing the patient's physiological load, and at the same time can realize portable sleep monitoring, and has a wider application range.

Description

technical field [0001] The present invention relates to the field of sleep state cascade recognition, and more particularly, relates to a method for SAHS screening, respiratory mode and sleep state cascade recognition. Background technique [0002] The comprehensive information of shape, intensity, speed and rhythm presented by pulse wave can reflect many physiological and pathological blood flow characteristics in the human cardiovascular system. Extracting the physiological and pathological information of the human body from the pulse wave as the basis for clinical diagnosis and treatment has been valued by Chinese and foreign medical circles over the years. The pulse signal is a non-linear and non-stationary signal that is approximately periodic. Initially, time-domain analysis was the main method, such as the shape, peak value, angle, and area of ​​the ascending and descending branches of the pulse waveform. However, a lot of practice has shown that the information cont...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/08A61B5/00
CPCA61B5/08A61B5/4809A61B5/4812A61B5/4815A61B5/7267
Inventor 罗语溪曾令紫周国琳廖源源吴欣
Owner SUN YAT SEN UNIV
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