具有強健性之非線性特徵提取法應用於臨床醫學

Abstract

近年來，許多領域持續發現及探討身體系統的複雜性，透過分子生物與病理機轉的研究，這些複雜機轉最終很可能會形成一個完整理論。另一方面，生醫信號分析也從生理系統的複雜性與調控理論之中發展出一些方法，能提取複雜特質和數據之下的訊息。這些非線性特徵提取方法，能夠從模糊不清的訊息下找出非線性的特徵作為疾病的判斷依據，其中一些方式也被證實效果優於傳統方法。然而非線性特徵卻容易受到臨床條件和環境的限制，如有限的數據長度，資料偶有會有參差不齊或是雜訊干擾。除此之外，提取方法也會造成失真，所使用之方法也可能無法有效濾除其他因素影響，有時甚至會大大增加後續分析的困難。本研究提出數種強健性的改良方法用來識別臨床數據的非線性特徵，以滿足臨床要求。 第一個探討的非線性特徵是訊息在多尺度的相關性，衡量方式是用資訊理論中的熵及渾沌碎形理論中的尺度，以不同時間尺度下的相關性，衡量系統的複雜特質。其中一個應用是透過計算血管動脈脈搏波速資訊的多尺度相關性，於大尺度的計算中增加計算準確度的方法，病患的量測時間能大幅減少到12分鐘。本方法能以較小的樣本大小(即600個連續信號)，在區分健康、中年、糖尿病患之間，達到與傳統的方法(即1000個連續信號)同樣的靈敏度。 另一個應用是在心率變異度分析中多尺度相關性的計算，透過改良的方法來抵抗心律不整因素的干擾，用於辨別安裝葉克膜病患的存活率。這項研究中提出了一種新的方法，通過分析在不同時間尺度的符號時間序列的不規則性來估計信號的複雜性，能有效避免葉克膜病患頻繁發生的心律不整所造成的干擾，該方法能夠檢測心臟調節功能的降低，並避免治療充血性心臟衰竭和葉克膜重症患者更加惡化。研究結果顯示，在嚴重干擾又同時有大量異常數值的心跳序列中，本方法能夠可靠地評估其多尺度的複雜性，因此可以作為一個有效的臨床工具，用於監控重症患者的心率調節功能。 第二個探討的非線性特徵是動態系統軌道的特質，這是透過相位空間軌跡計算而得。其中一個應用是在剖腹分娩過程中，以非侵方式從母體腹部體表取得心電圖，再透過幾種強健方法的處理，得出胎兒心電圖。最後透過類週期特性將胎兒心跳辨識出來，並使用心率變異參數量化軌跡，以獲取剖腹產對胎兒心跳與神經系統的作用。這項研究結果顯示，麻醉前，麻醉後，和分娩前5分鐘心率變異都明顯上升，該方法能夠可靠地評估胎兒對手術的反應，未來可以作為一個臨床工具，用於監控剖腹分娩過程中胎兒的狀態。 另一個應用是利用非線性波形相似度分析方法用於心房電圖，以找出重要的複雜碎裂心房電圖區域供心房電燒手術之用。該方法首先利用軌道的特徵找出每段週期，然後計算相空間這些軌跡的統計特性(相似性指數)。研究結果顯示，相似性指數在電燒成功病患的複雜碎裂心房電圖區域上較高，此類病患的預後也較好，這暗示了複雜碎裂心房電圖區域中相似性指數高的部分跟心房振顫的產生與維持有關聯。

In recent years, the complexity of human body has been continuous revealed and discussed in many fields, it may eventually lead to a complete theory through the studies on pathogenesis and molecular biology of disease. On the other hand, the complex theory combined with the homeostasis mechanism has been used for biomedical signal analysis trying to identify such complex phenomena and underlying information behind the clinic data. These methods can help to extract non-linear feature from ambiguous information as the disease assessments, some of them have been accepted to have more advantages than traditional ones. However, such refining procedure are subject to many restrictions in clinical conditions and environments, such as limited data length, information may be occasional uneven or noise interfered. In addition, the extraction itself can also lead to distortions, the interference from other mechanism may not be effectively removed which raised the difficulty on the subsequent analysis. Therefore, this thesis proposes several robust methods to identify the specific nonlinear features in clinic data series and try to fulfill the clinical requirements. The first portion of nonlinear feature is quantization of multi-scale correlation. It was derived from the entropy in information theory as well as the coarse-graining in chaos-fractal theory to quantify the complexity of a system through the correlations at different time scale. In the first study, a novel approach has been proposed to decrease the length of data in complexity calculation of pulse wave velocity (PWV) such that the time for data acquisition can be substantially reduced to 12 minutes. It utilized a smaller sample size (i.e. 600 consecutive signals) with remarkable preservation of sensitivity in differentiating among the healthy, aged, and diabetic populations compared with the conventional method (i.e. 1000 consecutive signals). The second study utilized the multi-scale correlation of heart beat intervals (RRI) on critical patients whose life continuation relies on extracorporeal membrane oxygenator (ECMO). This study propose a new approach to estimate the complexity in a signal by analyzing the irregularity of the sign time series of coarse-grained time series at different time scales. Without removing any outliers due to ectopic beats, the method is able to detect a degradation of cardiac control in patients with congestive heart failure and a more degradation. Moreover, the derived complexity measures can predict the mortality of ECMO patients. These results indicate that the proposed method may serve as a promising tool for monitoring cardiac function of patients in clinical settings. In the second portion of nonlinear feature, the trajectories on phase space have been used for calculating statistical properties of the orbits in a dynamic system. In the first study, a novel method been proposed to noninvasively derive the fetus ECG signals from the maternal abdominal ECG during the cesarean section (CS). The heart beat series derived from the noisy signal were then quantified by several heart rate variability (HRV) methods. Moat parameters tell that the HRV increased 5 minutes after anesthesia and 5 minutes before delivery. These results shows that the proposed method may serve as a promising tool to obtain significant information about the fetal condition during labor. In the second study, a nonlinear-based waveform similarity analysis of the local electrograms has been proposed, aiming to detect crucial complex fractionated atrial electrograms (CFEs) in atrial fibrillation (AF) ablation. This method firstly identify each cycle of orbits in the dynamic system and then calculate the statistical properties (similarity index, SI) of these trajectories on phase space. The result shows the average SI of the targeted CFEs was higher in termination patients, and they had a better outcome. This study suggested that sites with a high level of fibrillation electrogram similarity at the CFE sites were important for AF maintenance.