Sleep apnea syndrome detection system based on neural network

Abstract

The invention discloses a sleep apnea syndrome detection system based on LSTM neural network classification. The sleep apnea syndrome detection system is composed of a protection circuit, an impedancerespiration detection module, an electrocardiosignal detection module, an acceleration detection module, a mouth and nose respiration detection module, a signal self-encoding module, an LSTM featureextraction module, a wireless communication module, a LSTM neural network training device and a processing diagnosis device based on the LSTM neural network, a thermistor sensor is used for detectingmouth and nose breathing airflow, and a positive electrode and a negative electrode used for detecting respiratory and electrocardiogram signals of the chest of a human body. By using the system, hospitalization is not needed, the system is simple, and physiological and psychological burdens cannot be caused. The device can detect the respiratory states of different parts of a human body in multiple directions, achieves the purpose of sleep apnea syndrome classification diagnosis through an LSTM neural network classification algorithm, is simple to operate, and can be used at home.

Description

technical field [0001] The invention relates to the field of wearable intelligent diagnostic equipment, in particular to a sleep apnea syndrome detection system. Background technique [0002] Among the people who snore, about 20% of them often suffer from breath-holding symptoms when sleeping, which is medically called sleep apnea syndrome. Sleep apnea syndrome (sleep apnea syndrome, SAS) refers to the occurrence of apnea and (or) hypopnea in the sleep state due to various reasons, causing repeated intermittent hypoxemia and hypercapnia episodes, resulting in the occurrence of A clinical syndrome of a series of pathophysiological changes. According to the survey, about 40 million people in my country suffer from the disease at present. People suffering from this disease are prone to symptoms such as daytime sleepiness, irritability, fatigue, and decreased work efficiency, and are prone to high blood pressure, coronary heart disease, and cerebrovascular disease. The contin...

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Problems solved by technology

[0007] The problem in the above-mentioned prior art is that the user needs to adjust the tightness of the strap by himself. If it is too loose, the small breathing movement cannot be measured, and if it is too tight, it will cause discomfort to the user and affect normal breathing. Exercise will affect the accuracy of measurement; the use of blood oxygen probes cannot directly detect respiratory movement, and apnea events can only be detected when the apnea is prolonged and the blood oxygen saturation drops significantly. Studies have shown that only a few Apneic events of the

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