| Summary: | 博士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 96 === In this dissertation, a digital wireless electroencephalograph (EEG) acquisition and recording system was adopted to analyze the obstructive sleep apnea syndrome and investigate the relation between the EEG signal rhythms and Ryodoraku. The EEG acquisition and recording system uses a Bluetooth chip module and an energy-saving MSP430 Microcontroller (MCU) with powerful functions, along with integrated pre-amplifiers, filters, gain amplifiers, and a digital control circuit. After quickly acquiring and digitizing EEG signals, this system transfers the signals to a PC via the Bluetooth module. The PC then uses the NAB (Non-linear energy operator, AR model, and Bisecting k-means algorithm) method and bisecting k-means algorithm to classify and analyze patients'' EEG signals. The system first performs long-period EEG signal classification and storage, and then applies wavelet transforms to acquire EEG signal characteristics due to obstructive sleep apnea syndrome (OSAS). We trained a characteristic part analysis (CPA) artificial neural network designed by our group so that it could analyze and interpret the occurrence of OSAS, and compile and assess data. The system had a maximum sensitivity of approximately 69.64%, and a specificity of approximately 44.44%. This indicates that the system can provide clinical medical personnel with a valuable auxiliary diagnostic tool, improving medical service efficiency.
In addition, this research study the correlation analysis of EEG signal rhythms and Ryodoraku value of 12 acupuncture meridian of human body in Alpha wave and Beta wave brain activity periods, respectively. The experimental results have been confirmed that the Ryodoraku value had obvious difference in different brain wave periods which could provide reference for clinical medical personnel.
Keywords: Artificial Neural Network, Bluetooth, EEG, Ryodoraku, Obstructive Sleep Apnea Syndrome, Wavelet Transform.
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