Preliminary Study On The Monitoring Of Sleep Apnea Syndrome By Bioradar

Heart beat and respiration can be monitored through clothing and beddingusing the technology of bioradar, which can reduce the uncomfortable feeling ofthe patients without any electrodes, wires and sensors touching the human body,compared with the traditionally contact methods. Especially for the seriouslyburned victims and infectious disease patients, the bioradar was widely used inclinical while the contact monitor was limited.Sleep Apnea Syndrome(SAS) is a common disease which has a great harmto the health. Polysomnography (PSG) is the gold standard for the diagnosis ofthe SAS,but it needs some wires directly touching the skin of the patients,whichcan make the patients inconvenient and uncomfortable during long-termrecording. So we proposed to distinguish the normal sleep and the SAS bybioradar technology. The study was mainly focused on the following work.1. Studied the reliability of the breathing signal acquired by bioradarEstablished the simultaneous monitoring system by bioradar method and bandage-style method, and calculated the correlation relationship between thebreathing signal acquired by bioradar and the bandage-style. The results showedthe bioradar is reliable for our further study.2. Denoised the breathing signal acquired by bioradarDuring the long time monitoring of the breathing signal by bioradar, therewould be some other signals or noise that interfered the breathing signal. So inorder to extract the pure breathing signal, the low-pass FIR filters designed byKaiser window and by equal-ripple approximation were used and compared theireffects each other.3. Analyzed the characteristic of the breathing signal acquired by bioradarFor the normal breathing signal acquired by bioradar, we extracted themaximum value and minimum value of each breathing cycle and calculate thebreathing energy during a certain time. The ratio of the breathing could beacquired by calculating the interval of the adjacent maximum values, and thestrength of the breath could be reflected by calculating the energy of each periodof breathing signal.4. Identified the normal sleep breath from SASAccording to the characteristics of SAS, we simulated the SAS and acquiredthe signal of SAS by bioradar. Then pattern recognition method was used todistinguish the SAS and normal sleep breath.The innovations of this study were listed below：1. Filtered the breathing signal acquired by bioradar through low-pass FIRfilter designed by equal-ripple approximation method could reach the same effectof other filters with much lower filter order.2. Extracted the characteristics of normal sleep breathing signal and SAS signal, and distinguished the two state using the component at a certain frequencyin the spectrum, short-term average margin, short-term variance.