Breathing abnormity detection method, device and facility based on heart rate variability

Abstract

The invention discloses a breathing abnormity detection method, a breathing abnormity detection device and a breathing abnormity detection facility based on heart rate variability. The method comprises the following steps: collecting electrocardiosignals at first preset time under the night sleep state of a tested person; extracting heartbeat interphase of the collected electrocardiosignals for forming a heart rate variability sequence, and randomly dividing each level of the heart rate variability sequence into two groups with the same amount; cutting out a continuous second preset time slicesequence from the heart rate variability sequence and extracting two characteristic parameters of the heart rate variability sequence; and carrying out statistic analysis on the two characteristic parameters of the training group, and establishing a breathing abnormity detection model based on the neural network by adopting the extracted two characteristic parameters as the input, inputting the two characteristic parameters corresponding to the test group into the breathing abnormity detection model, so that breathing abnormity detection and evaluation can be carried out the tested person. According to the technical scheme, through the human body electrocardiosignals collected in a non-invasive manner, the apnea is analyzed by utilizing the heart rate variability sequence analysis technology, the operation is simple, and the applicability is good.

Description

technical field [0001] The invention relates to the field of breath detection, in particular to a method, device and equipment for detecting abnormal breath based on heart rate variability. Background technique [0002] Sleep apnea syndrome (sleep apnea syndrome) is a relatively common sleep-respiratory disease, clinical manifestations include nocturnal sleep snoring with apnea and daytime sleepiness. Recurrent nocturnal hypoxia and hypercapnia caused by apnea can lead to complications such as hypertension, coronary heart disease, diabetes and cerebrovascular disease, traffic accidents, and even sudden death at night. In the current clinical diagnosis, polysomnography (PSG) is the most important method for diagnosing OSAHS. It can not only judge the severity of the disease, but also comprehensively evaluate the patient's sleep structure, sleep apnea, hypoxia, and Changes in heart rate and blood pressure. However, its monitoring takes a long time and is not easy to wear and...

AI Technical Summary

Problems solved by technology

However, its monitoring takes a long time and is not easy to wear and the price is relatively high, so it is not convenient for the popularization of the whole range

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