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Method for determining severity of obstructive sleep apnea hypopnea syndrome (OSAHS) according to snore acoustic characteristics

A sleep apnea and severity technology, applied in the evaluation of respiratory organs, etc., can solve the problems of reducing patient comfort, discounting the practicality and universality of PSG equipment, and not correctly reflecting the patient's state, achieving low requirements and low cost. Inexpensive and easy to detect

Inactive Publication Date: 2011-08-03
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Depend on figure 1 It can be seen that while PSG provides accurate diagnosis, it also reduces the patient's comfort. What's worse, sometimes the PSG monitoring results cannot correctly reflect the patient's usual sleep status due to changes in the sleeping environment (no sensors are attached to the body during sleep). state
In addition, PSG monitoring is expensive, PSG equipment is not portable, requires professionals to connect cumbersome lines and analyze data of multiple signals throughout the night, which greatly reduces the practicability and universality of PSG equipment
[0003] Snoring is one of the most prominent features of OSAHS. Although PSG has a patch microphone signal to monitor the vibration of the larynx, it can only judge whether there is snoring based on the signal.

Method used

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  • Method for determining severity of obstructive sleep apnea hypopnea syndrome (OSAHS) according to snore acoustic characteristics
  • Method for determining severity of obstructive sleep apnea hypopnea syndrome (OSAHS) according to snore acoustic characteristics
  • Method for determining severity of obstructive sleep apnea hypopnea syndrome (OSAHS) according to snore acoustic characteristics

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Experimental program
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Effect test

Embodiment 1

[0021] Embodiment one: see Figure 4 , Figure 5 and Figure 6 , determine the severity of obstructive sleep apnea and hypopnea syndrome (OSAHS) according to the acoustic characteristics of snoring, characterized in that:

[0022] (1) Establish four reference models with different OSAHS severities: count the formant distribution of different OSAHS severities, and establish four reference models with different OSAHS severities according to the SBFP characteristic parameters, namely: simple snoring model, mild Moderate OSAHS model, moderate OSAHS model and severe OSAHS model;

[0023] (2) Detect snoring and calculate its SBFP characteristic parameters: record the subject's snoring, and calculate the SBFP characteristic parameters of its snoring;

[0024] (3) Match the SBFP characteristic parameters with four reference models: match the four reference models in step (1) according to the SBFP acoustic characteristic parameters of the subject, and the model with the largest matc...

Embodiment 2

[0026] Embodiment Two: This embodiment is basically the same as Embodiment Two, and the special features are as follows:

[0027] The step (1) needs to establish four reference models with different OSAHS severities:

[0028] 1) Obtain a large number of snoring sounds with different OSAHS severities, among which different OSAHS severities are obtained from the PSG monitoring results of the hospital, and the PSG monitoring results must be correct; each type of OSAHS experiment needs more than 500, that is, patients with four OSAHS levels at least 500 people each; for OSAHS patients, it is necessary to combine the MicL signal in Alice to synchronize the Alice signal with the snoring sound, and intercept the corresponding snoring sound inspiratory segment according to the apnea hypopnea position calibrated by medical diagnosis, as the experimental data of statistical acoustic laws; For patients with simple snoring, the inspiratory segments of all snoring sounds can be us...

Embodiment 3

[0036] Such as Figure 4 As shown, there are four steps to determine the severity of OSAHS according to the SBFP acoustic characteristics of snoring:

[0037] (1) "Off-line analysis" of the acoustic laws of different OSAHS severity levels, and four models of different OSAHS severity levels are established; (here based on the existing data analysis rules, rather than real-time recording data analysis, so it is called offline analysis)

[0038] (2) "Online analysis" calculates the SBFP characteristic parameters of the subjects; (online analysis refers to the processing of real-time recording data)

[0039] (3) four models of the off-line analysis in the SBFP matching (1) that are calculated in the step (2), determine the model that the subject matches according to the maximum probability;

[0040] (4) Results display: Determine the severity of OSAHS according to the matching model, and display the severity of OSAHS, the number of snoring sounds and the corresponding time.

[0...

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Abstract

The invention relates to a method for determining severity of an obstructive sleep apnea hypopnea syndrome (OSAHS) according to snore acoustic characteristics. The method comprises the following steps of: performing cluster analysis on format probability in sub-band (SBFP) parameters of a large number of experimental samples with the same OSAHS severity to obtain models representing four OSAHS severities: a simple snore mode, a mild OSAHS model, a moderate OSAHS model and a severe OSAHS model; and when a subject needs to be judged to which OSAHS severity the subject belongs, recording sound data of the subject all night long only, calculating an SBFP characteristic parameter, and matching the SBFP with the four models, so that which OSAHS severity the subject belongs to can be judged according to the model corresponding to the maximum matching probability. The snore when the subject sleeps is needed to be recorded only, so the method is simple and convenient without influencing the sleep quality of the patient.

Description

technical field [0001] The invention relates to a method for determining the severity of Obstructive sleep apnea hypopnea syndrome (OSAHS for short). Different from the traditional polysomnography system (PSG), the present invention determines the severity of OSAHS by analyzing the acoustic feature of "formant probability in sub-band (SBFP)" of snoring. To provide patients with preliminary diagnostic results and provide reference for the next step of surgery. Background technique [0002] In medicine, PSG is the gold standard for judging the severity of OSAHS. It is mainly based on multi-channel sensor signals (electrooculogram, electroencephalogram, electrocardiogram, electromyogram, nasal airflow, chest movement, abdominal movement, changes in blood oxygen) by professionals. etc.) Comprehensively judge the type of apnea (central type, obstructive type or mixed type) and the severity of OSAHS. Medically, the Apnea Hypopnea Index (AHI) indicates the severity of OSAHS. Ac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/08
Inventor 侯丽敏杜敏殷善开谢愫宋伟傅双英
Owner SHANGHAI UNIV
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