| Summary: | 博士 === 國立臺灣大學 === 電機工程學研究所 === 90 === The major purpose of this dissertation is to investigate the theories of various time-frequency analysis (TFA) and its capabilities in representing noninvasive physiological signals. The applications of TFAs in cutaneous electrogastrography (EGG) measurement and otoacoustic emissions (OAE) are also demonstrated. Owing to the tiny, noisy and nonstationary characteristics of noninvasive physiological signals, conventional time- and frequency- domain based analysis are not adequate to extract all the information embedded within the original signals. TFAs can effectively decompose the original signals into time-frequency distributions (TFDs) that can provide both time and frequency resolutions. More precise medical diagnosis can thus be achieved. Because TFAs can represent signal features more efficiently, higher performance is accomplished in several biomedical applications, such as signal compressions, and pattern recognitions, by TFA-based signal processing methodologies. The mathematical backgrounds of several commonly used linear and quadratic TFAs are described, and their pros and cons of representing nonstationary signals are discussed by apply simulated signals. Fast algorithms of the digital wavelet transform are introduced and proposed as the appropriate basis for real-time TFA-based signal processing, which are successfully implemented in a digital signal processor (DSP).
In the research of cutaneous EGG measurement, a microprocessor-based portable multichannel EGG monitoring system is proposed to record long-term EGG signals. A simulated EGG signal is designed and applied by the TFAs, and we concluded that the short-time Fourier transform (STFT) and Choi-Williams distributions are appropriate for EGG analysis. The slow wave can thus be precisely tracked by these TFAs, and quantitative parameters are proposed. Because it may generate errors by traditional power estimation, TFA-based power estimation, called multibands analysis, is developed in this dissertation. Clinical experiments are also deployed to evaluate the proposed EGG measurement system.
In the research of OAE measurement, a DSP-based instrument is developed for OAE monitoring. We used a simulated TEOAE signal to testify that the TFAs can efficiently decompose the original signal, and the results of various TFAs are compared and discussed. The specific feature of how different frequency components vary with time, which is similar to the Cochlear organ, can be successfully extracted by the wavelet transform. Because the acquired TEOAE signals are severely contaminated by environmental white noise, we designed a TFA-based active denoising methodology, called wavelet shrinkage, to suppress the embedded white noise during the measurement. The proposed method is more efficient than traditional statistically averaging method and is implemented in the DSP-based system.
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