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研究生:曾偉哲
研究生(外文):Wei-jhe Zeng
論文名稱:使用無線感測器網路傳送心電資料
論文名稱(外文):Transferring ECG data through wireless sensor network
指導教授:張光瓊
指導教授(外文):Kuang-chiung Chang
學位類別:碩士
校院名稱:龍華科技大學
系所名稱:電機工程研究所
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:2011
畢業學年度:99
語文別:中文
論文頁數:47
中文關鍵詞:分類GreyART無線感測器網路心電資料
外文關鍵詞:classificationGreyARTWireless sensor networksECG data
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感測器網路可用於各種應用地區。文中提出了一個心電圖遠程監護系統用於感測器網路,在病人身上穿戴移動式心電圖機器收集心電訊號,利用自適應共振理論(ART)類型的網路,將收集來的心跳分類,將異常心跳訊號藉由感測器網路發送到接收端。
自適應共振理論(ART)類型的網路。當在GreyART是用來分類不同的數據集的數據量,測量兩個實際的數據,可能因為在測量所參考的數據不同。在這種情況下,灰色關聯度不是全域的方式。由於測量各不相同,在GreyART,很難用一個固定的警戒門檻值確定當前輸入的數據是否屬於現有的群組或成為一個模板。如果沒有就在線上建立新的一群組,用於心跳分類。
這種分類兩個階段;一種是離線學習階段。在建議的研究成果指標,建立警戒門檻值。另一種是網上檢查階段,分類輸入心跳訊號。在這一階段,警戒門檻值和初始聚類中心是最佳的獲得在學習階段。
GreyART網路實現心跳訊號,分類減少資料的傳遞,達到節能的目的。
In this study, we propose an ECG (electrocardiogram) telemonitoring system applied in a rest house envirment. ECG signals are acquired and fed into an SOC (system on chip). On the SOC, an adaptive resonant theory (ART) type network is equipped to classify the ECG signals. As an abnormal heartbeat is identified, the SOC transfers the heartbeat, through the wireless sensor network, to the PC for further analysis. The routing protocol of the wireless sensor network used was developed in the previous study. The functions of the routing protocol include the establishment and maintenance of the network topology, the increase and deletion of the sensor node, and the guarantee of the quality of service of the wireless sensor network.
The proposed ECG beat classification involves two phases. One is the off-line learning phase. With the proposed performance index, the product of the classification accuracy and the partition quality, an optimal value for the vigilance threshold and the corresponding cluster centers from the learning results can be determined. The other is the online examining phase, which classifies the input ECG beats. In this phase, the vigilance threshold value and the initial cluster centers are the optimal ones obtained in the learning phase. Under these conditions, the GreyART network enables real-time classification of ECG beats
摘要 i
ABSTRACT ii
致謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 2
1.3 研究方法 4
第二章 無線感測器網路 8
2.1 泛流 8
2.2 Gossiping 10
2.3 SPIN 11
2.4 定向擴散 12
2.5 網路架構 13
2.6 無線感測器硬體 14
2.7 無線感測器網路通訊協定 15
第三章 心電訊號 19
3.1 心電訊號的生理基礎 19
3.2 心電圖 20
3.3 心跳類別與介紹 21
第四章 分類器設計 27
4.1 灰色關聯分析 28
4.2 GreyART network 29
4.3 心電圖資料 30
第五章 心跳分類方法 34
5.1 心電圖心跳分類方法 34
5.2 離線學習階段 34
5.3 在線上檢查階段 35
5.4 離線學習的結果 36
5.5 實驗結果 37
5.6 學習成果 37
第六章 結論 44
參考文獻 45
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