本研究發展的新方法係以熱力學的角度，提供一個簡單明確的方法，使用於研究生醫系統的行為，並將該行為量化，並能對系統行為的機轉加以研究。
「近似熵（approximate entropy）」是一個熱力學相關的方法，可使用於評估信號的不規則性。透過「移動近似熵（moving approximate entropy）」或「逆向移動近似熵（reverse moving approximate entropy）」對「時間序列（time series）」信號加以分析，可以觀察該時間序列的「局部不規則性」。若對二維或更高維度的信號加以分析即可觀察空間中的局部不規則性。透過分析信號的局部不規則性，並結合「模式識別（pattern recognition）」技術，即可以解析系統行為及其機轉，並對特定事件的發生提供準確的預測。本研究將計算「移動近似熵」的過程看成是一種「轉換（transform）」，並將「近似熵」視為轉換的結果，可再進一步運算處理。
在使用「移動近似熵」分析人類全血之「電阻抗凝固曲線」時間序列中，交叉比較全血與寡血小板血漿之「電阻抗凝固曲線」之「移動近似熵值」，並加入阿斯匹靈（aspirin）作為壓制血小板活性之藥劑後 之「移動近似熵值」。可以清楚的知道熱力學中「熵」的觀念可被用來檢驗寡血小板血漿的品質，並將該品質量化；阿斯匹靈是否對該病人的血小板有確實的效用；該病人的血小板功能的評估（血小板本身形成白血栓的功能及其所分泌血小板因子對血漿中凝血內路徑的功能）；阿斯匹靈（抗血小板）及抗血液凝固（凝固因子）對整個凝血過程的影響。可用以全程凝血功能檢驗、病情監控及藥品開發。
在使用「移動近似熵」分析人類全血樣本於微管道中之流動過程時所測得之電容值改變之 時間序列 研究中，可以清楚的看出全血在管道中每一處的流動難易程度（熱力學特性），並可將該難易程度量化。亦可明顯分辨全血在管道中流動與血漿（與血球）分離後流動難易程度之不同。可應用於生醫晶片中各部功能的品質之量化評估（品管）；比較不同設計的效能好壞，晶片不同製程之效能評估等多用途。
本研究所發展之方法，其優點在於可以普遍化，對於非線性的複雜系統而言均可適用，提供對系統的行為進一步的瞭解及分析，並對其中特定事件的發生提供準確預測的可能性。其主要的缺點為無法直接解析「同時發生」的多個行為的事件（此乃熱力學的限制），必須配合其他擾動系統運作的方法使用或提高取樣頻率（sampling rate），並縮短移動的資料長度才能順利完成目的。本文並討論移動近似熵計算時參數之適用性，在足夠的取樣頻率下，移動的資料長度係由所要觀測的事件的時間尺度所決定。在良好的信號對噪音（signal-to-noise ratio）比值之下，參數 r 可以沿用近似熵分析中的建議，使用移動資料的0.1–0.25標準差即可滿足要求。
本研究提供ㄧ個熱力學相關的新方法，藉由移動近似熵的計算方法，對複雜的生醫系統提供一個沿著時間軸向的熱力學解析方式。藉由計算及分析時間序列信號的局部不規則性，本方法可以提供及時的系統熱力學觀測。本方法雖屬時間序列信號的研究，但有潛力可以推展至二維或更高維度的空間信號研究。

This study demonstrates a new method for analyzing and predicting the behavior of a biomedical system in thermodynamic aspect. This new methodology allows us to understand the system’s behavior and predict the occurrence of certain important events, to quantify the quality of the system’s behavior, and to explore the mechanism behind the system’s behavior. The approximate entropy (ApEn) analysis is an entropy-related concept and is a practical method to measure irregularities of a signal or a time series obtained from the system under study and can be treated as a transformation. Combined with pattern recognition techniques, we use moving ApEn (MApEn) to calculate local irregularities of a time series to study, monitor, and predict the behavior of a system.
By using moving ApEn on an electric impedance coagulography (sampling rate 0.2 Hz), we extract signals from complex chemical activities hidden in a process like blood coagulation. The activity of platelets and coagulation cascade can be seen clearly with exact time correlation estimated from the impedance coagulation curve.
By using moving ApEn analysis on the time series (capacitance), we monitored the behavior of human whole blood sample running through the microchannel and the occurrence of separation of plasma. We can see none of the channel’s turnings are the same. The difficulties of passing each turning can be quantified by the peak value of its approximate entropy. The valleys between the peaks are the measure of the difficulties of flow through each straight channel. The conditions before the separation of plasma and after the separation are easy to distinguish and quantify.
This study also discusses the choosing of arguments used in the algorithm of moving ApEn. Under proper signal-to-noise ratios, argument r, the build-in filter, can be set as suggested in the ApEn algorithm as a 0.1–0.25 standard deviation of the data points within the moving window. Under a satisfactory sampling rate, the width of moving window w can be determined by the time scale of target events studied and adjusted around to have a good result.
This study demonstrates a non-parametric application of approximate entropy algorithm as a transformation tool to extract the thermodynamic profile of a biomedical system over time. With a modification, this method can be also adapted to a system of higher dimensions.

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