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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3773完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正(Yen-Jen Oyang) | |
| dc.contributor.author | Jen-Yee Hong | en |
| dc.contributor.author | 洪任諭 | zh_TW |
| dc.date.accessioned | 2021-05-13T08:36:38Z | - |
| dc.date.available | 2021-05-13T08:36:38Z | - |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-08-10 | |
| dc.identifier.citation | [1] B. J. Aehlert. ECGs Made Easy. Elsevier Health Sciences, 5th edition, 2015.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3773 | - |
| dc.description.abstract | 突發性院外心跳停止是引發成人死亡的首要原因之一,經常由心
室顫動 (VF) 造成。即時偵測這些致命性的心律不整,並且盡早以自動 體外電擊器 (AED) 施予去顫,是治療關鍵。過去的研究提出了各種偵 測心室顫動的演算法,但是大部分並未遵循現行由美國心臟病協會所 制定的醫學標準。本論文呈現了一個基於支撐向量機的機器學習演算 法,並在演算法的發展和測試過程當中,謹慎的依循美國心臟病協會 的醫學標準。整體而言,此演算法滿足美國心臟病協會標準要求的性 能,達到 93.21 % 的敏感度、99.88 % 的特異性、以及 89.28 % 的精確 度。此外,本研究使用的測試資料,比起過去研究更為全面,並且由 內科醫師檢視過確保正確性。因此,對於未來自動體外去顫器演算法 的研究,本資料集或許可作為一個更好的測試標準。 | zh_TW |
| dc.description.abstract | Sudden out-of-hospital cardiac arrest, one of the leading causes of death
among adults, is frequently caused by ventricular fibrillation (VF). Prompt recognition of these life-threatening arrhythmias and early defibrillation treat- ment using an automated external defibrillator (AED) are crucial. Previous researchers proposed various VF detection algorithms, but most of them did not comply with the existing medical standards for AED development set by the American Heart Association (AHA). This thesis presents a machine- learning AED algorithm based on support vector machine. The development and evaluation processes of the algorithm carefully followed the AHA med- ical standards. With an overall sensitivity of 93.21 %, specificity 99.88 %, and precision of 89.28 %, the proposed algorithm satisfied all of the perfor- mance goals required by the AHA guideline. In addition, the dataset used in our study was more comprehensive then that used in previous studies and was reviewed by a physician to ensure its correctness. Therefore, it might be a better benchmark for future researches of AED algorithms. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-13T08:36:38Z (GMT). No. of bitstreams: 1 ntu-105-P03922004-1.pdf: 2075311 bytes, checksum: 24df02236220a8a64269d211ae337c6c (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 誌謝iii
Acknowledgements v 中文摘要vii Abstract ix 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Methodology 11 2.1 Datasets Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.1 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.1 Measure Amplitudes . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 Asystole Detection . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.3 Labelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.4 Data Cleaning and Correction . . . . . . . . . . . . . . . . . . . 16 2.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.1 Time-Domain Features . . . . . . . . . . . . . . . . . . . . . . . 20 2.3.2 QRS Detector-Based Features . . . . . . . . . . . . . . . . . . . 25 2.3.3 Frequency-Domain Features . . . . . . . . . . . . . . . . . . . . 27 2.3.4 Complexity Measure-Based Features . . . . . . . . . . . . . . . 30 2.3.5 Phase Space Reconstruction . . . . . . . . . . . . . . . . . . . . 35 2.4 Machine Learning Algorithms for Classification . . . . . . . . . . . . . . 36 2.4.1 Soft Margin Support Vector Machine (SVM) . . . . . . . . . . . 38 2.4.2 Multiclass Support Vector Machine . . . . . . . . . . . . . . . . 41 2.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.5.1 AHA Recommendations for Reporting Performance . . . . . . . 43 2.6 Parameter Tuning for Performance Optimization . . . . . . . . . . . . . 44 2.7 Testing the Machine Learning Classifier . . . . . . . . . . . . . . . . . . 44 2.8 Implementation of the System . . . . . . . . . . . . . . . . . . . . . . . 44 3 Results 47 3.1 Dataset Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 Performance of Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . 49 4 Discussions 53 4.1 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.1.1 Common Causes of Errors . . . . . . . . . . . . . . . . . . . . . 55 4.1.2 Special Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2 Potential Roles of Linear Models . . . . . . . . . . . . . . . . . . . . . . 62 4.3 Importance of Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.4 Limitations of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5 Conclusion 67 References 71 | |
| dc.language.iso | en | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 心律不整 | zh_TW |
| dc.subject | 心室顫動 | zh_TW |
| dc.subject | 自動體外去顫器 | zh_TW |
| dc.subject | 心電圖 | zh_TW |
| dc.subject | 訊號處理 | zh_TW |
| dc.subject | Arrhythmia | en |
| dc.subject | Electrocardiography | en |
| dc.subject | Automatic external defibrillator | en |
| dc.subject | Ventricular fibrillation | en |
| dc.subject | Machine learning | en |
| dc.subject | Signal processing | en |
| dc.title | 以機器學習演算法偵測致命性心律不整 | zh_TW |
| dc.title | Detecting Life-Threatening Arrhythmia with Machine Learning Algorithms | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 賴飛羆(Fei Pei Lai),孫維仁(Wei-Zen Sun),韓謝忱(Hsieh-Cheng Han) | |
| dc.subject.keyword | 心律不整,心室顫動,自動體外去顫器,心電圖,訊號處理,機器學習, | zh_TW |
| dc.subject.keyword | Arrhythmia,Ventricular fibrillation,Automatic external defibrillator,Electrocardiography,Signal processing,Machine learning, | en |
| dc.relation.page | 77 | |
| dc.identifier.doi | 10.6342/NTU201602152 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2016-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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