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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 趙福杉 | |
| dc.contributor.author | Chu-Hsuan LIN | en |
| dc.contributor.author | 林竹萱 | zh_TW |
| dc.date.accessioned | 2021-06-16T06:42:24Z | - |
| dc.date.available | 2017-07-31 | |
| dc.date.copyright | 2014-07-31 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-07-29 | |
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J., et al. 'Atrial fibrillation, atrial flutter and normal sinus rhythm discrimination by means of blind source separation and spectral parameters extraction.' Computers in Cardiology, 2002. IEEE, 2002. [40] Millet-Roig, J., et al. 'Surface-ECG atrial activity extraction via blind source separation: spectral validation.' Computers in Cardiology, 2002. IEEE, 2002. [41] Jyh-Shing Roger Jang, 'Machine Learning Toolbox', http://mirlab.org/jang/matlab/toolbox/machineLearning. [42] Whitney, A. Wayne. 'A direct method of nonparametric measurement selection.' Computers, IEEE Transactions on 100.9 (1971): 1100-1103. [43] Chang C-C and Lin C-J . LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm [44] Burges, Christopher JC. 'A tutorial on support vector machines for pattern recognition.' 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57343 | - |
| dc.description.abstract | 心房顫動為最常見的心律不整疾病之一，並存在著許多風險如中風或全身性血管栓塞，但心房顫動患者通常不會有明顯的症狀，容易被忽略而嚴重影響日後生活，因此如何早期準確檢測出心房顫動為一項重要課題。
本研究以三個心房顫動主要特徵(1)心臟跳動不規則(2)P波不明顯(3)心房活動關係為基礎，提出了一套特徵擷取及特徵選取方式，為避免心房顫動與其他心室上心律不整疾病混淆，使用了三種不同的資料庫進行比較分析，配合Support Vector Machine分類器以及交叉驗證方式，心房顫動及正常心電圖辨識率達95.67%，而心室上心律不整與正常心電圖辨識率則為96.67%。 | zh_TW |
| dc.description.abstract | Atrial fibrillation (AF) is a common arrhythmia that can lead several risks to people who suffer from the illness, such as stroke or heart failure. However, the patients do not have obvious symptom, making it easy to ignore and seriously affect the future of life. Therefore, early and truly detection of AF becomes an important issue.
In this study, we raise a feature extraction and feature selection method base on three main physiological characteristics of AF: (1) heart rate irregular (2) P wave unobvious, and (3) atrial activity relationship. In order to avoid AF and other supraventricular arrhythmia be confused, we use three different database for comparative analysis. Finally, the SVM classifier and cross validation method were used to discriminate between AF and Normal ECG with a 95.67% accuracy, and supraventricular arrhythmia, and Normal ECG with 96.67% accuracy by considering only three features. For the discrimination of three categories, a recognition rate of 92.23% was achieved. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T06:42:24Z (GMT). No. of bitstreams: 1 ntu-103-R01548025-1.pdf: 1996576 bytes, checksum: 197d24c0422aef905409fd1e1396ffb8 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 致謝… i
摘要… ii Abstract iii 目錄… iv 圖目錄 vii 表目錄 viii 專有名詞縮寫對照表 ix 一、 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 心室上心律不整簡介 3 1.3.1 心室上心律不整 3 1.3.2 心房顫動 4 1.3.3 心電圖於心房顫動檢測之困難 6 二、 研究方法 7 2.1 實驗資料庫 8 2.2 希爾伯特黃轉換(Hilbert Huang Transform, HHT) 8 2.2.1 經驗模態分解(Empirical mode decomposition, EMD) 8 2.2.2 希爾伯特轉換(Hilbert transform, HT) 9 2.3 訊號前處理 10 2.3.1 內插(Interpolation) 10 2.3.2 訊號去趨勢(Detrend) 10 2.3.3 移動平均濾波(Moving-average filter) 10 2.4 心電圖特徵點偵測 11 2.4.1 QRS complex之偵測 11 2.4.2 P波之偵測 12 2.5 心房活動擷取(Atrial Activity Extraction) 13 2.6 特徵擷取 14 2.6.1 心律變異分析(Heart rate variability, HRV) 14 2.6.2 其他時域特徵 15 2.6.3 熵(Entropy) 16 2.6.4 小波熵(Wavelet Entropy) 18 2.6.5 相位分佈(Phase distribution) 20 2.6.6 心房活動特徵 21 2.7 特徵挑選 22 2.7.1 逐次前饋式搜索法(Sequential Forward Selection, SFS) 22 2.7.2 窮舉法(Exhaustive method) 23 2.8 分類方式 24 2.8.1 交叉驗證(cross validation) 24 2.8.2 支持向量機(Support Vector Machine) 25 三、 實驗結果 26 3.1 實驗評估標準 26 3.2 單一特徵分析 27 3.3 辨別結果 28 3.3.1 辨別兩類疾病 28 3.3.2 辨別三類 31 3.4 其他資料庫驗證 32 四、 討論 33 4.1 其他資料庫測試 33 4.2 最佳特徵說明 33 4.3 其他文獻比較 34 五、 結論與未來發展 35 5.1 結論 35 5.2 未來發展 35 參考文獻 36 | |
| dc.language.iso | zh-TW | |
| dc.subject | 非線性特徵 | zh_TW |
| dc.subject | 心房顫動 | zh_TW |
| dc.subject | 心室上心律不整 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 希爾伯特-黃 轉換 | zh_TW |
| dc.subject | Hilbert-Huang transform | en |
| dc.subject | Atrial fibrillation | en |
| dc.subject | support vector machine | en |
| dc.subject | non-linear feature. | en |
| dc.subject | supraventricular arrhythmia | en |
| dc.title | 心房顫動與心室上心律不整之偵測 | zh_TW |
| dc.title | Detection of Atrial Fibrillation and Supraventricular arrhythmia | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 廖文劍 | |
| dc.contributor.oralexamcommittee | 郭德勝,黃基礎,謝建興,曾乙立 | |
| dc.subject.keyword | 心房顫動,心室上心律不整,支持向量機,希爾伯特-黃 轉換,非線性特徵, | zh_TW |
| dc.subject.keyword | Atrial fibrillation,supraventricular arrhythmia,support vector machine,Hilbert-Huang transform,non-linear feature., | en |
| dc.relation.page | 39 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-07-29 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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