To provide a method for screening patients with sleep apnea hypopnea syndrome (SAHS)

the heart rate variability (HRV) was applied to the classification of sleep respiratory events. The probabilistic neural network (PNN) was proposed to classify the normal and abnormal sleep respiratory events according to the HRV features to achieve the purpose of preliminary screening of the disease. In this classification process

the HRV features of ECG were firstly extracted from the polysomnographic monitoring data related to the normal and abnormal sleep respiratory events

and then normalized as the features. Then

PNN classification algorithm was used to train and classify the features. The prediction and classification performance of the model was finally evaluated. The results of the PNN classifier for the accuracy

sensitivity

specificity

area under the receiver operating characteristic curve (AUC) of the subjects and time consumption for classification were respectively: 75.97%

82.51%

76.22%

0.7936 and 0.63 s. Compared with generalized regression neural network (GRNN) and extreme learning machine (ELM) classification algorithms

PNN classification algorithm is optimal in sensitivity

specificity

AUC and time-consumptions. In this study

HRV and PNN classification algorithm were used to classify the presence or absence of abnormal sleep respiratory events

thus providing a low physiological load SAHS screening method. The study has a certain practical significance for the initial screening of the disease.