基于短时能量的呼吸暂停信号识别方法

呼吸暂停是一种常见的疾病,严重的呼吸暂停会导致患者猝死。针对患者需要对睡眠过程中呼吸信号进行实时监测,提出了一种基于短时能量的呼吸暂停信号识别监测方法。该方法基于患者呼吸过程的信号频域特性,先对不同的患者进行自适应鼾声特征信号建模;之后通过神经网络信号识别方法,利用建立的模型对患者的呼吸信号进行呼吸暂停判断。实验结果表明,对不同的患者进行睡眠呼吸过程进行监测时,可以识别95%的呼吸暂停信号,本方法为呼吸暂停患者的实时监测提供了一种高精度的信号识别方法。

Short-term energy based apnea signal recognition method

Apnea is a common condition in which severe apnea can cause sudden death. Aiming at the need of patients to monitor the respiratory signal during sleep, a method based on short-term energy for apnea signal recognition and monitoring is proposed. The method is based on the frequency domain characteristics of the patient"s respiratory process, and the adaptive chirp characteristic signal is firstly modeled for different patients; The neural network signal recognition method is used to make an apnea judgment on the patient"s respiratory signal based on the established model. The experimental results show that monitoring the sleep and breathing process of different patients can identify 95% of the apnea signal. This method provides a high-precision signal recognition method for real-time monitoring of apnea patients. The experimental results show that monitoring the sleep and breathing process of different patients can identify 95% of the apnea signal. This method provides a high-precision signal recognition method for real-time monitoring of apnea patients.