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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 施博仁 | zh_TW |
| dc.contributor.advisor | Po-Jen Shih | en |
| dc.contributor.author | 陳盈璇 | zh_TW |
| dc.contributor.author | Ying-Syuan Chen | en |
| dc.date.accessioned | 2025-09-10T16:11:32Z | - |
| dc.date.available | 2025-09-11 | - |
| dc.date.copyright | 2025-09-10 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-23 | - |
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Physics Letters A, 1996. 223(4): p. 255–260. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99406 | - |
| dc.description.abstract | 本研究旨在針對節律異常之脈搏波訊號，建立具辨識能力與生理意涵之非線性量化指標。傳統分析方法如時域與頻域分析，雖廣泛應用於心血管訊號，卻高度仰賴波形完整性，對於結構紊亂的脈搏波往往難以提供穩定且具解釋力的結果。有鑑於此，本研究聚焦於節律異常且結構混亂的脈搏波，探究其中是否潛藏可量化且具辨識潛力的非線性動態特徵。本研究以非線性分析工具中的遞迴圖、最大李亞普諾夫指數（MLE）與龐加萊映射為核心方法，系統性分析健康、心律不整與臨終三組對象之脈搏波動態特性，特別聚焦於心律不整與臨終患者之異常波形。研究結果顯示，多數遞迴圖參數（RR、DET、ENTR、Lmax）能有效反映系統週期性，而垂直線參數（LAM、TT）未能展現停滯性特徵，反與週期性變化相關，顯示其作為停滯性指標的適用性有限。而MLE與龐加萊映射亦能輔助評估脈搏波系統的混沌程度與波形週期性。綜合所有指標進行機器學習分類，三種多分類模型之分類精確度達82 %，此結果顯示非線性量化指標具一定程度的脈搏波分類能力。這些指標不僅在學術層面上補足傳統分析在異常節律脈搏波處理上的解釋侷限，亦有望在臨床應用中提供具量化依據與生理解釋能力的判別工具，有助於心律不整之診斷與病情追蹤，以及臨終狀態的早期預警，提升臨床決策的精確性與反應效率。 | zh_TW |
| dc.description.abstract | This study aims to develop nonlinear quantitative indices with both discriminative power and physiological relevance for analyzing pulse wave signals characterized by rhythm abnormalities. Traditional time- and frequency-domain analyses are widely used in cardiovascular signal processing but rely heavily on waveform integrity, often leading to unreliable results for structurally disordered pulse waves. To address this limitation, the present study focuses on pulse signals with rhythm abnormalities and disrupted structures, particularly those from patients with arrhythmia and individuals in terminal stages, to investigate whether they contain quantifiable nonlinear dynamic features. Key analytical methods include recurrence plots, the maximum Lyapunov exponent (MLE), and Poincaré maps, applied to data from healthy , arrhythmia patients, and terminally ill patients. Results show that most recurrence-based parameters (RR, DET, ENTR, Lmax) effectively reflect signal periodicity, while vertical line parameters (LAM, TT) do not indicate laminarity as expected and instead show associations with periodic changes, suggesting limited suitability as laminarity markers. MLE and Poincaré maps further assist in characterizing chaotic behavior and rhythm stability. Integrating these indices into machine learning models yielded a classification accuracy of 82%, demonstrating their potential for distinguishing physiological conditions.These findings not only extend the analytical scope beyond conventional methods but also offer clinically meaningful tools for diagnosing arrhythmia, tracking disease progression, and providing early warning of terminal decline, thereby improving the precision and timeliness of clinical decisions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-10T16:11:32Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-10T16:11:32Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
謝誌 ii 中文摘要 iii ABSTRACT iv 目次 v 圖次 viii 表次 xi 第一章 緒論 1 1.1 研究動機 1 1.2 橈動脈脈搏波介紹 2 1.2.1 脈搏波的量測工具 2 1.2.2 心律不整與臨終患者的脈搏波 4 1.3 研究目的與重要性 7 第二章 脈搏波非線性分析方法文獻回顧 8 2.1 脈搏波傳統分析方法 8 2.2 混沌理論 11 2.2.1 混沌系統 12 2.2.2 吸引子 15 2.2.3 分岔（Bifurcation） 18 2.3 脈搏波非線性分析方法 20 2.3.1 時間延遲嵌入定理 20 2.3.2 遞迴圖 22 2.3.3 李亞普諾夫指數 26 2.3.4 龐加萊圖與龐加萊映射 30 2.4 脈搏波非線性特徵之研究應用 35 第三章 研究方法 38 3.1 受試者脈搏波訊號蒐集 39 3.1.1 資料集來源 39 3.1.2 後設分類與篩選 39 3.2 非線性分析方法 40 3.2.1 訊號預處理、相空間重建與正規化 40 3.2.2 遞迴圖 46 3.2.3 李亞普諾夫指數 49 3.2.4 龐加萊映射 51 3.3 參數統計與分類 57 第四章 研究結果 59 4.1 相空間重建與正規化 59 4.2 遞迴圖 63 4.3 李亞普諾夫指數 70 4.4 龐加萊映射 72 4.5 參數相關性分析 74 4.6 機器學習分類與特徵重要性 76 第五章 討論 80 5.1 遞迴圖 80 5.1.1 遞迴圖量化參數統計結果之討論 80 5.1.2 遞迴圖整體結構之討論 88 5.1.3 心率對遞迴參數之影響 88 5.1.4 重搏波（dicrotic wave）對遞迴圖的影響 91 5.1.5 不同輸入訊號對遞迴圖結構之影響 92 5.2 最大李亞普諾夫指數 93 5.3 龐加萊映射 96 5.4 特徵參數統計與組別區辨能力摘要 96 5.5 參數相關性分析 97 5.6 機器學習分類與特徵重要性 98 5.7 研究限制 100 第六章 結論與未來展望 102 6.1 研究結論 102 6.2 未來展望 103 參考資料 105 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 脈搏波 | zh_TW |
| dc.subject | 非線性分析 | zh_TW |
| dc.subject | 節律異常 | zh_TW |
| dc.subject | 心律不整 | zh_TW |
| dc.subject | 臨終 | zh_TW |
| dc.subject | Arrhythmia | en |
| dc.subject | Pulse wave | en |
| dc.subject | Nonlinear analysis | en |
| dc.subject | Terminal stage | en |
| dc.subject | Rhythm abnormality | en |
| dc.title | 節律異常之橈動脈脈搏波非線性動態特徵之量化分析 | zh_TW |
| dc.title | Quantitative Analysis of the Nonlinear Dynamic Characteristics in Radial Artery Pulse Waves with Rhythm Abnormalities | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 郭育誠;田孟軒;施華儒 | zh_TW |
| dc.contributor.oralexamcommittee | Yu-Cheng Kuo;Meng-Hsuan Tien;Hua-Ju Shih | en |
| dc.subject.keyword | 脈搏波,非線性分析,節律異常,心律不整,臨終, | zh_TW |
| dc.subject.keyword | Pulse wave,Nonlinear analysis,Rhythm abnormality,Arrhythmia,Terminal stage, | en |
| dc.relation.page | 109 | - |
| dc.identifier.doi | 10.6342/NTU202502005 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-07-25 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 醫學工程學系 | - |
| dc.date.embargo-lift | 2027-08-01 | - |
| 顯示於系所單位： | 醫學工程學研究所 | |
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