A signal detection system for judging sleep apnea

Abstract

The present invention provides a signal detection method and system for judging sleep apnea. The signal detection method includes the following steps: acquiring the vital sign signal of the user during sleep; performing structured processing on the vital sign signal of the user during sleep to remove invalid Get the effective sign signal set; extract and use the multi-dimensional morphological features of the sleep breathing sample signal to perform feature training on the initial model of the classifier to obtain the sleep breathing detection model; input the effective sign signal set into the sleep breathing detection model for signal processing to obtain the user's sleep Probability data for occurrence of apnea. The signal detection method for judging sleep apnea uses multi-dimensional morphological features to perform feature training on the initial model of the classifier, so that the performance of the sleep apnea detection model is more robust, and a more accurate probability of apnea occurring when the user sleeps can be obtained data, so that it is convenient to judge whether a sleep apnea event has occurred.

Description

technical field [0001] The invention relates to the research field of sleep breathing signals, in particular to a signal detection system for judging sleep apnea. Background technique [0002] Sleep is an important physiological activity that plays a key role in the physical and spiritual self-recovery of the human body. In recent years, with the acceleration of people's life rhythm and the increase of work pressure, people's awareness of their own health is increasing, and various portable medical testing equipment has been popularized in home life applications. However, general portable detection equipment has a relatively simple processing method for the acquired vital sign signals, and its classification methods and feature engineering detection methods are mostly limited to single-dimensional empirical statistics, and multi-dimensional features are not combined to effectively train the signal detection model. Therefore, General portable medical detection equipment has ...

AI Technical Summary

Problems solved by technology

However, general portable detection equipment has a relatively simple processing method for the acquired vital sign signals, and its classification methods and feature engineering detection methods are mostly limited to single-dimensional empirical statistics, and multi-dimensional features are not combined to effectively train the signal detection model. Therefore, General portable medical detection equipment has the problem that the classification accuracy is not high, and the acquired probability data of apnea during sleep is not accurate enough, which will directly affect the doctor's judgment on whether apnea occurs during sleep.

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