Ambulatory blood pressure feature extraction method for OSAHS

Abstract

The invention discloses an ambulatory blood pressure feature extraction method for obstructive sleep apnea syndrome (OSAHS). The method comprises the following steps: extracting the following parameters from 24-hour ambulatory blood pressure monitoring data of a subject, comprising: at least a part of: nighttime blood pressure rise effective height, nighttime blood pressure rise maximum height, nighttime blood pressure rise duration, nighttime blood pressure rise slope, nighttime blood pressure fluctuation frequency, nighttime blood pressure base line upper area, nighttime blood pressure base line lower area, nighttime blood pressure drop effective height, nighttime blood pressure drop maximum depth, nighttime blood pressure drop duration, a nighttime blood pressure decrease slope, a nighttime blood pressure secondary increase duration, a nighttime blood pressure secondary increase slope, a nighttime blood pressure fluctuation frequency, a morning peak blood pressure increase maximum height, a morning peak blood pressure increase duration, a morning peak blood pressure increase slope, a morning peak blood pressure decrease duration, a morning peak blood pressure decrease slope, and an area on a morning peak blood pressure baseline.

Description

technical field [0001] The invention relates to the field of sleep breathing, in particular to a dynamic blood pressure feature extraction method for OSAHS. Background technique [0002] Obstructive sleep apnea hypopnea syndrome (OSAHS) refers to patients who repeatedly experience apnea and hypopnea during sleep. Clinically, it can be manifested as snoring, and the snoring sound is irregular. Being suffocated is often accompanied by a series of symptoms such as increased nocturia, morning headache, dizziness, and dry mouth and throat. In recent years, the onset characteristics of OSAHS have shown a trend towards younger and non-obese people, and it has become one of the main diseases that affect the quality of human sleep. At present, the clinical diagnosis of OSHAS is still based on polysomnography (PSG). However, PSG examination requires patients to wear corresponding equipment in a specific sleep laboratory. The examination conditions are strict, and patient compliance a...

AI Technical Summary

Problems solved by technology

There are methods of OSAHS identification through ECG signals, cardiopulmonary coupling signals, etc., but these methods have not yet entered clinical first-line diagnostic scenarios.

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