臺灣博碩士論文加值系統

生命徵象檢測的準確度與OHCA(out-of-cardiac arrest)患者的存活率有著密不可分的關係。若是能盡早實施CPR(cardiopulmonary resuscitation)，可有效地提高OHCA患者的存活率。然而，目前國際上仍是倚賴手指觸摸頸動脈的方式來辨識突發性昏迷者是否存在生命徵象。但是，即便是專業的醫護人員，也無法在短時間內有自信地說出判斷的結果。在本篇論文中，我們利用活體人類才擁有的脈搏週期訊號特性與人體全身心脈變化一致性的概念，提出一套新穎的非接觸式脈搏量測之活體皮膚辨識演算法。藉由擷取人體臉部兩塊不同區域的rPPG(remote Photoplethysmography)訊號，除了可用於計算脈搏率之外，也結合本論文提出的PCD(pulse-correlation-deviation)演算法，有效地分辨活體/非活體訊號。經過多個實驗證實，所有受測者的瞬時脈搏率在統計學之95%信賴區間中平均誤差為+2.2 ~ -2.02 bpm(beat per minute)，而在本論文提出的活體皮膚檢測實驗中，靜態實驗與動態實驗可個別獲得95%與90%的準確度。最後，本論文將此技術實現於智慧型手機中，不僅提供一般民眾一個便利的生理訊號監測平台，亦是可隨身攜帶、即時使用的生命偵測器，用以協助辨識突發性心脈停止並盡早實施CPR與體外電擊提升患者的存活率，並與救護隊聯絡時可明確地說明患者的情況，增加高階救護隊派遣的正確率。

The poor recognition of out-of-hospital cardiac arrest (OHCA) with checking carotid pulse is less than 50% correct by the public. Thus, to assist public for more effective recognition of cardiac arrest and to facilitate patients receiving early resuscitation, we propose a pulse-correlation-deviation (PCD) method to differentiate the living/non-living skin tissue by analyzing the contactless pulse signal. We firstly constructed the identification method on the smartphone, which is ubiquitous and eligible to accord realistic emergency demands. The image sensor on the smartphone was utilized to capture the remote PPG (rPPG) physiological signal and a cross-correlation operation was used to enhance the characteristics of cardiac condition of a living subject. The results show that the proposed PCD method outperforms the existed method (FDR) by providing faster and higher detection rates (95% and 90% for the fixed-holding and hand-holding smartphone experiment, respectively). In the future, the proposed innovation can facilitate the bystander to recognize OHCA in short time (within 8 seconds), and to execute earlier CPR, earlier PAD, and to provide more information for emergency calls to improve the survival rate of OHCA patients.

摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章、 緒論 1
1.1 動機與目的 1
1.2 文獻探討 3
1.3 相關論文比較 5
1.4 論文架構 6
第二章、 背景與原理 7
2.1 背景 7
2.2 參考文獻方法之描述 8
2.3 PPG定義與原理 10
2.4 傳統PPG訊號量測 11
2.5 非接觸式量測與發展 12
2.6 臉部偵測 13
2.7 物件追蹤 14
2.8 雜訊來源 17
2.9 PPG相關生理參數應用 17
第三章、 研究方法 18
3.1 系統介紹 18
3.2 ROI選擇與脈搏訊號擷取 20
3.2.1 臉部偵測 & ROI追蹤 20
3.2.2 ROI訊號擷取 22
3.3 訊號處理 23
3.3.1 帶通濾波器 23
3.3.2 GRD演算法 24
3.3.3 卡爾曼濾波 27
3.4 脈搏訊號估計 29
3.4.1 波峰偵測 29
3.4.2 脈搏率計算 30
3.5 活體皮膚辨識演算法 31
3.5.1 Cross-correlation 31
3.5.2 Zero-crossing 33
3.5.3 Classification 35
第四章、 實驗方法與結果討論 36
4.1 實驗流程 36
4.2 實驗設計與結果 37
4.2.1 實驗一 37
4.2.2 實驗二 39
4.2.3 實驗三 42
4.3 結果討論 45
4.3.1 雜訊影響 45
4.3.2 訊號擷取時間 45
4.3.3 量測限制 46
第五章、 結論與未來展望 47
參考文獻 48

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