生物醫學系統在近幾年已經被快速的研究和討論，並且廣泛的應用至人類生活中。在這生物科學快速發展的世紀，許多在醫學工程領域的研究人員開發出許多新的診斷和治療工具於特定的疾病上，像是提出新的醫療電子電路架構或訊號處理演算法來輔助診斷或治療人類的疾病。
醫療儀器像是一個媒介連接著生物和電子，主要用來量化和擷取其生物特徵或行為模式，並進一步探討生物演進和變化的過程。一般來說，典型的醫療儀器由前端感測器傳遞生物特性後，再由類比訊號放大器和類比數位轉換器將類比訊號轉換成數位訊號，轉換後的數位訊號可以直接在硬體上執行訊號特徵擷取之外也可以傳送至電腦做長時間的資料儲存或高準確性的數學運算和分析，而資料的傳送方式可依據應用的需求選擇有線或無線的傳輸。舉例來說，我們提出了一個即時的閉迴路刺激系統，此系統結合數位訊號處理以及信息資料分析方法，分析後的訊號特徵可透過無線藍芽傳輸與行動裝置連結。另外，閉迴路系統啟動刺激器的條件可透過數位核心電路即時運算腦神經訊號相位同步的特性後，控制前端類比刺激電路，此方法的好處可降低70.1%的刺激器電量消耗，也可以降低整體系統的功率消耗，延長電池的使用壽命。
在本篇論文中，電子訊號的輸出從類比電路的訊號處理、類比訊號數位化至數位訊號處理，其中數位訊號處理的方法可分為瞬時傅立葉轉換(short-time Fourier Transform)、相位同步分析、心律變異度的線性分析、多尺度摘分析(multiscale entropy)和去趨勢波動分析方法(detrended fluctuation analysis)。基本上瞬時傅立葉轉換和相位同步分析主要用來分析短時間內的電生理訊號變化特性，而心律變異度的線性分析方法主要可分為時間和頻率領域的分析，除了線性分析方法之外本論文也採用兩種新開發的非線性分析方法，第一種為多尺度摘分析方法，可提供連續時間資料序列的規律圖型分析，可有效的量化單獨訊號之間的相關性、複雜度和相互作用關係。第二種方法為去趨勢波動分析，主要分析電訊號中的碎形特性來量化生理訊號。最後上述的各種訊號分析參數都可以透過統計方法來驗證其結果和關聯性，並且可建立數學模型去預測電生理訊號未來的特性。例如，本論文使用change-score analysis和generalized additive models的統計方法，結合分析後的電生理訊號參數，透過回歸模型的分析和機器學習的技術(machine learning technique)來評估人體放鬆狀態，除此之外，為了更進一步的與穿戴式裝置整合，本論文還提出了僅需要使用心電訊號就能達到初步的生理狀態分析的數學模型。
總結本論文討論的內容包含生醫系統的電子電路、訊號處理模組和生醫信息，首先將電子電路中用來量測電訊號的類比電路和數位電路整合成積體電路，可有效縮小系統設計的面積和功率消耗，因此所開發與選擇的演算法條件都必須符合低複雜度、低運算時間以及高準確性。除此之外，數位電路內有包含微處理器可讓訊號處理的演算法在設計過程中更有彈性和可適應性。最後分析出來的電訊號特性都有透過統計分析檢定，並估測運算後的生醫訊號指標參數和臨床信息之間的關聯性與顯著性，雖然這些參數都有可能會受到外在雜訊或者人為因素的干擾而產生誤差，但是本論文使用多變數選擇演算法可以準確的挑出對於數學模型內最有意義的參數使用，同時在實驗中也給予生醫系統設計過程使用的變數與定義給予適當的限制，避免過度擬合(overfitting)。最後本論文共提出兩種實驗結果，第一種為即時閉迴路腦神經相位同步偵測系統，其特性已在上述段落描述，另外一個實驗為生醫信息資料分析，主要透過腦電訊號和心電訊號分析後的指標參數建立可預測人體放鬆狀態的數學模型，此模型內共使用兩種分析方法為change-score analysis和generalized additive models，準確率分別為80.7%和86.5%。

Biomedical systems have expanded markedly in recent years, spreading to most aspects of human life. In promoting rapid advancements in biological science, which have led to the creation of novel electrical circuits and signal processing methods for developing tools for diagnosing and treating human diseases, several researchers in biomedical engineering have developed new tools for specific medical conditions.
Electronic instruments provide an interface between biology and electronics. Such interfaces enable quantifying and characterizing biological phenomena, which can then be investigated to elucidate biological processes. A typical interface comprises a sensor or electrode for detecting biological parameters, the signals of which can then be amplified and converted into the digital domain. These digital data can be processed by hardware or transferred to a personal computer for long-term storage and more accurate signal processing. Depending on the application requirements, the data can be transferred through a wired or wireless link. For instance, we propose a real-time closed-loop neurostimulation system and off-line bioinformatics data-analysis method. A neurostimulation system can be measured on the basis of neural phase synchrony by using real-time electrical processing. In addition, the closed-loop phase synchrony detection requires 70.1% electrical energy to be delivered to the deep brain, which can reduce the system power consumption and extend the battery life.
The electrical signals output from an analog circuit are processed by signal processing through a data acquisition unit, signal conditioning block, and digital signal processing program. In this dissertation, the digital signal processing program features a short-time Fourier transform, phase coherence analysis, linear analysis of heart rate variability, multiscale entropy analysis, and detrended fluctuation analysis. The short-time Fourier transform and phase coherence analysis were used to determine the bioelectrical activity of an object. Analysis of linear heart rate variability, which represents one of the most promising autonomic activity markers, was performed in the time and frequency domains. In addition to conducting a linear analysis, we also propose two innovative analysis methods derived from nonlinear and nonstationary processes. The first method is a multiscale entropy measurement that provides the regularity pattern of a time series by analyzing the interplay between quantitative connotations and the correlations among individual electrical signals. The second method, detrended fluctuation analysis, is employed to evaluate the fractal correlations causing heart rate fluctuations that originate from the interactive regulatory mechanisms. Moreover, most processed biometric signals can be statistically analyzed to estimate effects and predict outcomes. For example, this dissertation presents a method requiring purely biometric values derived from a regression model and machine learning technique, which are referred to as change-score analysis and generalized additive models (GAMs), to evaluate relaxing states. Furthermore, considering the practical applications for wearable sensor nodes, EEG data obtained before stress were not used in the change-score analysis.
A biomedical system comprising electronic instruments, signal processing modules, and bioinformatics is discussed in this dissertation. The analog front end and digital circuits for detecting and acquiring electrical signals were developed using integrated circuits. The guidelines for developing the signal processing algorithms were low complexity, short latency, high sensitivity, and accurate characterization. Additionally, a microprocessor was used to ensure that the electronic algorithm design is flexible and adaptable. Finally, a statistical analysis method for estimating the correlations between biometric values and clinical informatics is presented. Even when the parameters are irregular and change in the external environment, the variable selection method can accurately extract the signals of interest. In this dissertation, the experimental results of the biomedical system were compared with various performance criteria. The comparison showed that the proposed real-time closed-loop neurostimulation system attains the required low energy for stimulating deep brain activity. In the bioinformatics data analysis, the change-score analysis and GAMs respectively achieved an overall accuracy of 80.7% and 86.5% in recognizing changes in brain waves.

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