Abstract
Nowadays, the implementation of biomedical integrated systems are attracting more attention than before due to the emerging industry of wearable devices and the rapid growth of elderly population. In particular, efficient biomedical signal processing systems are in demand for various applications. This dissertation aims to develop advanced digital signal processing (DSP) systems for wireless biomedical applications. Many problems in the field of biomedical signal processing can be reduced to a task of feature extraction and event detection. This kind of problem generally treats a set of measurements and asks for the recognition of some patterns. Through appropriate feature extraction from the targeted signal, we can develop efficient signal processing algorithms to perform different tasks. This dissertation proposes two digital signal processing biomedical systems. The first one is an electroencephalography (EEG) based brain-computer interface (BCI) utilizing eye commands. This system first uses a low-complexity edge detector to extract the sharp edges of the eye movement events. Then, we use pulse width demodulation (PWDM) to further classify the eye commands with only addition operations. Also, a training mechanism is proposed to facilitate the detection of eye commands. Users wearing an EEG headset can give six eye commands including glancing toward four directions and winking of the left or right eye. This proposed system is implemented with FPGA and achieves a detection rate of 89.7% in the experiments. The second proposed digital signal processing biomedical system is an ultra-wideband(UWB) radar signal processing platform for human respiratory feature extraction. In this system, we propose a new respiration model and an iterative correlation search algorithm with early termination to acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These features, the parameterized and compressed respiratory signals, can provide physical information to facilitate clinical diagnosis and help to manage a more efficient database for the respiration monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period. Moreover, the parameterized waveforms are used to construct a new diagnosis method for respiratory diseases in clinical trials.