目前，接受手術的病人，疼痛評估完全依賴病人主觀敘述，沒有客觀的居家儀器可用於監測術前、術中與術後的疼痛趨勢。尤其是對於有心臟病的患者，其麻醉後風險更高，更需要仔細追蹤疼痛狀況，防止因為疼痛治療的不良而誘發心臟病發。因此，本研究目標在參考術中疼痛監測機器(Analgesia Nociception Index，ANI)，發展出居家疼痛指數檢測原型機器，讓準備接受手術的病人可以得到完整的術前、術中與術後的麻醉評估疼痛趨勢，從而達到降低心血管疾病的風險，並探討其相對應的商業模式應用。
在現行的評鑑規範與醫療法規定下，醫療行為必須由醫護人員親自執行。然而，在Covid-19疫情期間，許多病人需要進行接觸隔離，這使得使用傳統有線型生命徵象監測儀進行手術麻醉時，存在著傳播病毒的風險。因此，Covid-19疫情加速了遠距醫療與遠距監測儀器的發展。
遠距監測儀器是一種安全、有效的監測方式，不僅避免病人與醫護人員的直接接觸，同時也提高了監測即時性與趨勢性。本研究聚焦於引進無線物聯網技術（Internet of Things，IoT）、快速傅立葉轉換(Fast Fourier Transfer，FFT)並且透過卷積神經網路（Convolutional Neural Network，CNN），以專家標籤危險心電圖波形與區別雜訊(Noise)作為病人術前、術中與術後返家的生命徵象與疼痛趨勢監測系統，以及探討引進新的遠距醫療技術之後的商業模式。
希望透過人工智慧技術的導入，可以實現自動化監測和警報系統，讓遠程醫療弭補術後訪視人力不足的缺口。該系統的研發可降低病人接受手術麻醉後返家的麻醉合併症，特別是對於有心臟病患者而言，提供了一個安心的監測方案。

Currently, the pain assessment of surgical patients relies entirely on subjective patient reporting, and there are no objective home devices available to monitor pain trends before, during, and after surgery. This is particularly concerning for patients with heart disease, as they are at higher risk after anesthesia and require careful monitoring to prevent adverse cardiac events triggered by inadequate pain management. Therefore, the objective of this study is to develop a home pain index prototype machine based on the reference of the Analgesia Nociception Index (ANI), a machine for intraoperative pain monitoring, which can provide surgical patients with a complete assessment of anesthesia and pain trends before, during, and after surgery. This will reduce the risk of cardiovascular disease and explore the corresponding commercial application model.
Under current evaluation standards and medical regulations, medical procedures must be performed by healthcare professionals in person. However, during the Covid-19 pandemic, many patients need to undergo contact isolation, which poses a risk of virus transmission when using traditional wired vital sign monitors during anesthesia for surgery. Therefore, Covid-19 has accelerated the development of remote medical care and remote monitoring devices.
Remote monitoring devices are a safe and effective monitoring method that not only avoids direct contact between patients and healthcare professionals, but also improves the real-time and trend monitoring. This study focuses on introducing wireless Internet of Things (IoT) technology, Fast Fourier Transfer (FFT), and Convolutional Neural Network (CNN) to tag expert-labelled dangerous electrocardiogram waveforms and distinguish noise as a life sign and pain trend monitoring system for patients before, during, and after surgery when returning home. It also explores the business model after introducing new remote medical technology.
The introduction of artificial intelligence technology is expected to achieve an automated monitoring and alarm system, filling the gap in postoperative visits where human resources are insufficient. The development of this system can reduce anesthesia-related complications for patients returning home after surgery, especially for those with heart disease, providing a reassuring monitoring solution.

論文審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
目錄 vi
圖次 viii
表次 x
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與範圍 4
第三節 論文架構 6
第二章 文獻探討 8
第一節 疼痛評估 8
第二節 遠距醫療 12
第三節 商業模式 14
第四節 E醫院麻醉科之相關評鑑規範 16
第三章 研究方法與流程 38
第一節 設計科學研究法 38
第二節 研究流程 40
第四章 計與發展解決方案 43
第一節 外在分析與機會威脅評估 43
第二節 內在分析與優勢劣勢評估 45
第三節 解決方案中疼痛監測原理 48
第五章 展示與評估解決方案 55
第一節 展示解決方案 55
第二節 評估解決方案 62
第三節 解決問題的競爭者方案 75
第六章 結論 76
第一節 研究成果 76
第二節 研究貢獻 77
第三節 研究限制 79
第四節 未來研究方向 81
參考文獻 83

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