Wireless Telemedicine Systems for Diagnosing Sleep Disorders with Zigbee Star Network Topology

摘要

Good sleep is critical for one's overall physical and mental health but more than 50 million Americans have experienced or are suffering from sleep disorders. Nevertheless, 85% of them remain undiagnosed or untreated. They can lead to chronic diseases. Sleep disorders are diagnosed through polysomnography, also known as sleep study, performed in a sleep laboratory overnight. This perturbs his/her daily sleep routine, and consequently, an accurate diagnosis cannot be made. Many companies have been developing home sleep test systems to reduce the cost of sleep studies and provide a more convenience solution to patients. The category of the system varies as type Ⅱ, type III and type Ⅳ according to the type of sleep study. Current systems cannot be easily extended from one type to include a higher type. A patient who has a type Ⅳ system to diagnose sleep apnea should additionally purchase a type Ⅱ system which has functions that overlap with a type Ⅲ system, to evaluate sleep stages. In this paper, we propose a wireless telemedicine system for easy extension of channels using the start network topology of the Zigbee protocol. The HST system consists of two wireless HST devices with a Zigbee module, a wireless HST receiver with both a Zigbee and a Wi-Fi module, and a sever which monitors/saves the physiological signals. One transmitter provides 5 channels for 2x EOG, 2x EEG and EMG to evaluate sleep stages. The other transmitter provides 5 additional channels for ECG, nasal air flow, body position, abdominal/chest efforts and oxygen saturation to diagnose sleep apnea. These two transmitters, acting as routers, and the receiver as a coordinator form a Zigbee star network. The data from each transmitter in the receiver are retransmitted to the monitoring unit through Wi-Fi. By building a star network with Zigbee, channels can be easily extended so that low level systems can be upgraded to higher level systems by simply adding the necessary channels. In addition, the proposed system provides real time monitoring of physiological signals at remote locations using Wi-Fi.