| Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data | |
|---|---|
| 學年 | 102 |
| 學期 | 2 |
| 發表日期 | 2014-05-13 |
| 作品名稱 | Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data |
| 作品名稱（其他語言） | |
| 著者 | Weng, Julia Tzu-Ya; Lin, Jyun-Jie; Chen, Yi-Cheng; Chang, Pei-Chann |
| 作品所屬單位 | 淡江大學資訊工程學系 |
| 出版者 | Springner |
| 會議名稱 | The 1st International Workshop on Pattern Mining and Application of Big Data (BigPMA 2014) (in conjunction with PAKDD 2014) |
| 會議地點 | Tainan, Taiwan |
| 摘要 | Rapid and accurate diagnosis of patients with acute myocardial infarction is vital. The ST segment in Electrocardiography (ECG) represents the change of electric potential during the period from the end of ventricular depolarization to the beginning of repolarization and plays an important role in the detection of myocardial infarction. However, ECG monitoring generates big volumes of data and the underlying complexity must be extracted by a combination of methods. This study combines the advantages of polynomial approximation and principal component analysis. The proposed approach is stable for the 12-lead ECG data collected from the PTB database and achieves an accuracy of 98.07%. |
| 關鍵字 | 12-lead ECG;Myocardial infarction;Principal component;Polynomial approximation analysis;Support vector machine |
| 語言 | en_US |
| 收錄於 | EI |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20140513~20140516 |
| 通訊作者 | Julia Tzu-Ya Weng |
| 國別 | TWN |
| 公開徵稿 | Y |
| 出版型式 | 電子版 |
| 出處 | Lecture Notes in Artificial Intelligence 8643, pp.689-699 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/99573 ) |
GoogleAnalytics