Method and system for apnea event detection based on BCG signal

Abstract

The invention discloses a method and system for detecting apnea events based on BCG signals, which are used to accurately detect apnea events during sleep. This method locates the potential occurrence interval of respiratory events by identifying the arousal segment, and then divides the event potential occurrence interval into apnea segment, respiratory effort segment, and arousal segment, and then extracts fine-grained features that can describe the breathing pattern from the three segments. , and finally with the help of machine learning methods, it is judged whether the potential occurrence stage of the event contains an apnea event. The system mainly includes: a signal acquisition module, a data processing module, and a detection result output module. The method and system can automatically and accurately locate the potential occurrence interval of apnea events and automatically divide the interval into three different stages, which facilitates the description of breathing patterns from multiple aspects and in a fine-grained manner, and greatly improves the detection accuracy of apnea events Rate.

Description

technical field [0001] The present invention relates to the field of biomedicine, in particular to a method and system for detecting an apnea event. Background technique [0002] In recent years, sleep disorders have become an important factor affecting people's quality of life. Among them, sleep apnea syndrome has become the most common sleep disorder. According to the relevant data of the World Health Organization, about 200 million people in the world suffer from sleep apnea syndrome, accounting for about 2%-4% of the world's total population. The high incidence and serious harm of sleep apnea syndrome ranks first among all kinds of sleep disorders, and the detection of apnea events has become a problem that needs to be studied. [0003] Existing apnea event detection methods are mainly divided into two categories: 1) apnea event detection based on PSG, chest strap, oximeter, airflow detector, etc.; 2) apnea event detection based on heart rate variability. The former r...

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Problems solved by technology

The former requires the subject to wear several instruments or electrodes during the test, which affects the comfort of the subject during sleep

The latter usually adopts an equal data segment strategy to divide the data into several segments of equal length. This strategy ignores the fact that apnea events may occur at any time and the duration is not fixed, which affects the accuracy of event detection.

[0004] In addition, the above two types of apnea event detection methods both take the possible interval of apnea events as a whole, and do not consider the internal structure of apnea events, so the extracted features cannot describe apnea events in a fine-grained manner, thereby reducing the probability of occurrence of apnea events. detection accuracy

[0005] For the above problems, no effective solutions have been proposed so far

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