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研究生:陳首儒
研究生(外文):Shou-Ru Chen
論文名稱:以主成分分析與模糊推論方法判斷心跳種類
論文名稱(外文):Determining Heartbeat Types by Principal Component Analysis and Fuzzy Inference
指導教授:黃有評黃有評引用關係
口試委員:張玉山練光祐李祖添
口試日期:2012-07-13
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:71
中文關鍵詞:模糊推論無線感測網路主成分分析心律不整
外文關鍵詞:Fuzzy inferencewireless sensor networkprincipal component analysisarrhythmia
相關次數:
  • 被引用被引用:1
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
近幾年由於飲食習慣的改變,心血管疾病患者的年齡層逐漸下降,心臟疾病所造成的死亡率越來越高,因此監測心臟疾病的技術扮演了重要的角色。傳統的心電圖檢查步驟繁瑣並且需要醫療人員的專業協助,當病患心臟發生輕微異常時,往往到醫院作詳細檢查時已恢復正常而查不出病因。本研究針對心跳監測設計一套適用於居家照護之心電圖即時檢測系統,使用心電圖感測器搭配無線感測網路技術來檢測使用者的心跳快慢與心律變動性的指標,並將檢測結果儲存到資料庫,本研究也整合雲端系統,以網頁方式呈現監測結果,達到輔助診斷的效果。此外,對於心律不整的心跳檢測,本研究使用美國麻省理工學院提供的MIT-BIH資料庫來分析,將常見的心律不整心跳種類NORM、LBBB、RBBB、VPC、APC及PB,使用主成分分析演算法來選取主要特徵,再將選取後的主要特徵以模糊推論方法設計心律不整偵測模組,依據不同心跳的特徵設計對應的模糊規則庫。為了有效降低系統運算複雜度,在不增加誤判率的原則下,將原本六項心跳特徵刪減至五項,並將相鄰的歸屬函數整合,由原本 條模糊規則組合簡化至 種組合。實驗結果顯示所提系統能達成正常心跳的脈搏數檢測,心律不整心跳 NORM、LBBB、RBBB、VPC、APC與PB之判別準確率分別為97.5%、87.5%、92.5%、100%、95%與100%。實驗結果驗證本研究所提之系統適用於心律不整心跳種類判斷。

For the past few years due to the changes in diet habits, patients with cardiovascular disease become progressively younger, and the rate of deaths caused by heart disease rises; therefore, using new technologies to monitor heart disease play an important role. Traditional ECG inspection procedures can be very complicated and need professional assistance. When minor abnormality occurs the patient’s heart often returns to normal before he/she takes a detailed examination. Thus, the cause of abnormality remains unsolved. In this study, we proposed a suitable design of heartbeat monitoring ECG real-time detection system for home care, which uses the ECG sensors and a wireless sensor network technology to detect the user''s heartbeat rates and their variations. This study also integrates with the cloud system, showing the monitoring results on web pages as diagnosing assistance. In addition, the Massachusetts Institute of Technology MIT-BIH database is used to analyze arrhythmia, which is used to select main features from common types of arrhythmia, namely, NORM, LBBB, RBBB, VPC, APC and PB. Based on the selected features an arrhythmia detection module is devised to detect the arrhythmia that is determined by the corresponding fuzzy rules. In order to reduce the system complexity without increasing the false positive rate, we cut heartbeat characteristics from six to five. Besides, the fuzzy rules are simplified from 66 to rules. The experimental results show that the proposed system is able to detect normal and arrhythmia heartbeat detection. For NORM, LBBB, RBBB, VPC, APC and PB the discriminative accuracy rates are 97.5%, 87.5%, 92.5%, 100%, 95% and 100%, respectively. The experimental result shows that the proposed system is suitable for distinguishing the types of arrhythmia heartbeat.

摘 要 i
ABSTRACT ii
致謝 iv
目錄 v
表目錄 vii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究方法 3
1.4 論文架構 3
第二章 相關技術及應用探討 5
2.1 無線感測網路技術 5
2.2 心電圖 8
2.2.1 心電圖波形 9
2.2.2 心電圖導聯與電極貼放位置 10
2.3 心律不整 12
2.4模糊理論 16
2.4.1 模糊集合 16
2.4.2模糊歸屬函數 17
2.5主成分分析演算法 19
2.6 開發環境 21
2.6.1 硬體 21
2.6.2 軟體 22
2.6.2.1 .NET Framework 22
2.6.2.2 ASP.NET 23
2.6.2.3 Vasual C# 2010 24
2.6.2.4 Cygwin 26
2.6.2.5 TinyOS 26
2.6.2.6 Microsoft SQL Server 2008 27
第三章 系統架構與設計 30
3.1 系統架構 30
3.1.1 硬體架構 30
3.1.1.1 ZigBee 無線感測器平台 31
3.1.1.2 心跳感測器與電極 33
3.1.2 軟體架構 35
3.2 系統流程與設計 37
3.2.1 心跳監測系統執行流程 37
3.2.2心律不整心跳判斷系統執行流程 38
第四章 實驗結果與分析 53
4.1 實驗環境 53
4.2 系統介面 54
4.3 實驗結果 58
4.3.1 心跳監測 58
4.3.2心律不整偵測實驗 60
第五章 結論與未來展望 66
5.1 結論 66
5.2 未來展望 67
參考文獻 68



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