| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 99 === Wheezes have often been treated as an important indicator to diagnose the obstructive pulmonary diseases. A rapid wheezing detection system may help physicians to analyze and to long-term monitor the patients’ situations. This thesis proposes a portable wheezing detection system based on Field Programmable Gate Array (FPGA). It accelerates wheezes detection. It could flexibility function as a single process system or be integrated with other biomedical signal detection system.
Firstly, the sound signal is segmented into units of 2 seconds. Then short-time Fourier transform was used to look into the relationship between the time and frequency components of the sound data. Thereafter, we continued processing the spectrogram by 2D bilateral filtering, edge detection, multi-threshold image segmentation, morphological image processing and image labeling to extract the wheezes features according to Computerized Respiratory Sound Analysis (CORSA) standards. Then these features were used to train Support Vector Machines (SVMs) and built the classification models. Finally, this trained model is used to distinguish to detect wheeze for new coming sound data.
This system runs on Xilinx ML605 platform. Experiment results show a high performance of 0.912 in analysis of wheeze recognition in hardware. The detection process is good for 51.97 MHz clock frequency. It is good for high speed classification for wheeze.
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