Research On Sleep Health Monitoring And Intervention Technology Based On Semantic Interoperability

Sleep is an important and complex process in human life,and it plays an important role in human activities.A good sleep helps to detoxify various organs.With the development of the economy and the improvement of living standards,people’s mental pressure is increasing and the quality of sleep is getting lower.Among them,sleep apnea syndrome is an important reason that affects the quality of sleep.Polysomnography(PSG),sophisticated heart rate and breathing monitoring bracelets and head-worn ventilators are now available to detect breathing problems.However,these monitoring and intervention devices are all contact devices,which may affect the quality of sleep.In addition,these devices operate independently,and data cannot be interconnected,multi-parameter fusion cannot be achieved,and more abundant applications cannot be provided.This paper mainly studies the interoperability information model and technology between contactless sleep monitoring devices and intervention devices for sleep apnea syndrome.This paper designs a specific method to realize the interoperability between sleep health monitoring intervention devices,that is,to realize the information interaction and semantic interoperability between sleep monitoring and intervention devices such as oximeters,non-contact smart mattresses and oxygen generators.Thereby,the improvement of breathing problems or sleep problems can be achieved in a non-interference and non-contact environment.This paper proposes a method for detecting sleep apnea syndrome by non-contact sleep monitoring equipment.After extracting the bsllistocardiogram signal(BCG)and respiratory signal,the features of heart rate variability(HRV)and cardiopulmonary coupling(CPC)are calculated.In this paper,advanced features are mined through Convolutional Neural Network(CNN),and then input to Bi-directional Long Short-Term Memory Network(BiLSTM).Secondly,this paper proposes a blood oxygen saturation reduction algorithm,which uses the medical characteristics of blood oxygen saturation to judge whether sleep apnea occurs or not based on the changes of characteristic parameters,which provides strong support for determining the occurrence of apnea events.This paper proposes an ontology-based semantic description model for sleep monitoring and intervention equipment.This paper semantically describes the basic information,status,and functions of oximeters,non-contact smart mattresses,and oxygen concentrator equipment,as well as semantically describes the results of human physiological parameters and basic information,correlations,and task constraints of monitoring and intervention tasks.For the discrimination of sleep apnea,the smart mattress and the oximeter are interoperated,and combined with the blood oxygen saturation events and the discrimination results of the smart mattress,a joint reasoning and decision-making scheme for multiple devices is proposed to improve the accuracy of the discrimination of sleep apnea syndrome.Using SWRL language to establish inference rule base,improve the accuracy of sleep apnea syndrome discrimination,so as to realize the intelligent intervention of apnea syndrome,and finally realize the unmanned monitoring closed-loop system of sleep health monitoring and intervention.