Adult respiratory event detection method based on time domain

Abstract

The invention relates to an adult respiratory event detection method based on a time domain. The problems of long time, non-uniform standard and low accuracy of artificial respiratory event discrimination in the prior art are solved. The method comprises the following steps: acquiring a mouth-nose airflow signal, a chest movement signal and an oxyhemoglobin saturation signal; performing time domain division analysis on the mouth-nose airflow signal, acquiring signal amplitude data and recording a signal event; preliminarily classifying the mouth-nose airflow signal events into temporary apnea events and temporary hypopnea events according to the mouth-nose airflow signals and the airflow snore signals; determining a hypopnea event by combining the provisional hypopnea event with the oxyhemoglobin saturation information; and determining the type of the apnea event by combining the temporary apnea event with the chest movement signal amplitude data comparison condition. Compared with manual breathing time judgment, the method has the advantages that the judgment time is shorter, the judgment standard is unified, the judgment structure is more accurate, and a structure can be quickly, accurately and automatically analyzed for reference of doctors.

Description

A time-domain based detection method for adult respiratory events technical field The present invention relates to the technical field of physiological parameter analysis, especially relate to a kind of adult respiratory event detection based on time domain. measurement method. Background technique [0002] As people pay more and more attention to sleep, sleep medicine has become a new hot spot in clinical medicine. sleeping Objective detection is an essential condition for sleep medicine and sleep-related research. Polysomnography (PSG) It is the internationally recognized gold standard for the diagnosis of sleep apnea-hypopnea syndrome. The discrimination of nocturnal sleep breathing events is important factor in the diagnosis of the disease. Respiratory events are divided into hypopnea and apnea, and apnea is divided into central apnea, obstructive Apnea, mixed apnea. The criteria for judging each respiratory event in AASM are as follows: Hypoventilation: ...

AI Technical Summary

Problems solved by technology

[0010] The present invention mainly solves the problems of long time, non-uniform standards, and low accuracy in artificially distinguishing respiratory events in the prior art, and provides a time-domain-based adult respiratory event detection method

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