以多維整體經驗模態分析法進行激發光-螢光矩陣之內在螢光特徵擷取

Abstract

激發光-螢光矩陣光譜為非侵入式組織檢測，且在臨床上越來越受重視，然 而，激發光-螢光矩陣光譜的內在特徵至今仍不清楚。光漂白特性與化學物質的 複雜組成份，造成不同物質的螢光光譜常有重疊干擾導致不易辨識。過去有許多 研究將主成分分析法用於激發光-螢光矩陣分析，以提取光譜中之特徵，並檢測 這些特徵與疾病的關聯性，但不同組織成分的光譜特徵並沒有被完全地分開或清 楚地定義。多維整體經驗模態分析法為近年來新提出的方法，此方法透過反覆減 去局部均值以對資料進行解構，可提取在多維空間尺度下的震盪且不會產生訊息 缺失。本研究的目的是利用多維整體經驗模態分析法提取激發光-螢光矩陣光譜 的內在特徵。我們先用模擬的訊號來檢視分析方法對與發光-螢光矩陣光譜相似 但較為單純的訊號的解構能力，之後再以此分析方法進行發光-螢光矩陣光譜之 解構。本研究亦使用主成分分析法與多維整體經驗模態分析法做比較。結果顯示， 主成分分析可呈現出與化學物相關的主要光譜特徵（以膠原蛋白為主），而多維 整體經驗模態分析法可進一步呈現光譜中各種螢光物質的獨立特徵，例如膠原蛋 白、為生素 D 等。整體而言，多維整體經驗模態分析法提供一個光譜分析的新觀 點，可提取光譜中螢光特徵，並增進生物螢光檢測的可能性。

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method decomposes data by subtracting local means iteratively and can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to extract the intrinsic characteristics of EEM by using MEEMD. We use simulated signal to examine the decomposition ability of MEEMD on decomposing signal which was similar to EEM but simpler than EEM, and then MEEMD was applied to decompose EEM. PCA was used to compare with MEEMD in this study. The results indicate that although PCA provides the main spectral features associated with chemical compounds, which mainly contributed by collagen, MEEMD can provide additional intrinsic features with more reliable mapping of individual chemical compounds, e.g. collagen and vitamin D. Overall, MEEMD provide a new point of view on EEM analysis and has the potential to extract intrinsic fluorescence features and improve the detection of biological fluorophores.of individual chemical compounds, e.g. collagen and vitamin D. Overall, MEEMD provide a new point of view on EEM analysis and has the potential to extract intrinsic fluorescence features and improve the detection of biological fluorophores.