ABSTRACT
In this study we made full use of the heart rate spectrum analysis to obtain the character parameters of heart rate variability related to EEG sleep phase information, and then we discarded the correlation between characteristics by employing Principal Component Analysis. Finally, by means of the decision tree based on Fisher rules, we established two full automatic models for identifying healthy people and sleep apnea hypopnea syndroma (SAHS) patients respectively. The result of experiments indicates that the model is accurate and robust.
Subject(s)
Female , Humans , Male , Decision Trees , Electrocardiography , Electroencephalography , Heart Rate , Physiology , Models, Biological , Polysomnography , Signal Processing, Computer-Assisted , Sleep , Physiology , Sleep Apnea Syndromes , Sleep StagesABSTRACT
Objective To develop a portable sleep monitoring system with low burden. Methods Information from signals related with sleep were used and dug. Electrodes from patients' body were reduced to decrease patient's physiological and psychological burden. Results Polysomnography(PSG) is the gold standard in detecting the Sleep Apnea Hypopnea Syndrome disease at present. While this method has its problems, such as complicated operation, large expenditure and big physiology and psychology burden for the patient. A sleep monitor system was introduced which has the advantages of convenience, practicality, small physiology and psychology burden for the patient. Conclusion The result indicates that this device is warmly welcomed by doctors and patients, and it is a new device for the diagnosis of the SAHS disease.
ABSTRACT
Objective To design a wireless monitoring system which can obtain patients' multi-parameters (such as ECG, respiratory, blood pressure, SPO2, and heart rate). Methods The wireless communication system, based on CDMA, receives data through internet and analyses ECG by dealing and measuring signals. Results A wireless monitoring system is designed, which can analyse ECG, blood pressure, and respiratory. Doctors can give diagnostic suggestion based on the monitoring system's analyses and send it to patients by short messages. Conclusion This system can realize long -distance medical monitoring and report the diagnosis to patients timely.