Abstract
The goal of this thesis is to develop a portable system for long-term monitoring the respiratory disorder effects. With the implemented system, the doctors are able to collect the data about the overnight breathing patterns. The primary study of this research focuses on building a prototype platform, including the wireless data hub and sensory nodes for sleep apnea symptom (SAS) detections. The wireless hub has amplifiers, filters, analog-to-digital converters (ADC), and a Bluetooth module. The sensors contain microphones and accelerometer senor. In this prototype, the accelerometer senor is used for effort detection measurements. The Principle Component Analysis (PCA) can extract rotation matrix from the data of accelerometers to maximize the signal dynamic and improve the linearity. All the data collected from this invasive detection system will be transmitted to the cloud database. Automatic syndrome extraction and classification algorithms are developing to help the efficiently report the respiratory status of nighttime.