Design and Implementation of Embedded Systems for Biomedical Applications

• Main Authors: You-DeLiu, 劉又德
• Other Authors: Da-Wei Chang
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/68302375894115224547

博士 === 國立成功大學 === 資訊工程學系 === 102 === Recording of physiological signals plays an important role in the biomedical research and clinical fields. In this study, two embedded systems related to physiological signal recording were proposed. Both systems are based on microcontrollers (MCUs) and wireless technologies. The first system is a wireless modularized Polysomnography (PSG) system. PSG continuously and simultaneously records multiple physiological signals during the sleep of the subject, and it is commonly used in hospitals or sleep centers to diagnose sleep disorders. The excessive number of wired connections for traditional PSG is often a problem that leads to sleep disturbance. The proposed wireless PSG system is composed of multiple tiny, low-cost and wireless-synchronized signal acquisition nodes, and each node acquires specific physiological signals within a small body region to reduce sleep disturbance resulting from recording wires. To evaluate accuracy, the system and a commercial PSG system were mounted on subjects to simultaneously perform overnight recording, and the recorded data were compared. The results show that, in addition to high consistency (〉93%) with the reference system, due to the reduction of the disturbance from recording wires, the proposed system has better comfortableness performance in terms of several objective and subjective sleep indices. The second system is a wireless mobile neurofeecback training system. Neurofeedback training is recently used in enhancement of cognitive function or amelioration of clinical symptoms. Most available neurofeedback systems are a laboratory design, which contains wires to the training machine, resulting in inconvenience for subjects. The proposed wireless neurofeecback system contains an EEG signal analysis device and a smartphone connected based on the Bluetooth low energy (BLE) technology. A three-stage neurofeedback experiment was performed to evaluate the performance of the mobile neurofeecback training system.