Name
Title | ||
|---|---|---|
Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data. |
Abstract | ||
|---|---|---|
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 %. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-13186-3_61 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
12-lead ECG,Myocardial infarction,Principal component analysis,Polynomial approximation,Support vector machine | Myocardial infarction,Data mining,ST segment,Polynomial,Pattern recognition,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Repolarization,Electrocardiography,Principal component analysis | Conference |
Volume | ISSN | Citations |
8643 | 0302-9743 | 3 |
PageRank | References | Authors |
0.41 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tzu-Ya Weng | 1 | 20 | 5.40 |
| Jyun-Jie Lin | 2 | 150 | 7.31 |
| Yi-Cheng Chen | 3 | 187 | 12.72 |
| Pei-Chann Chang | 4 | 1752 | 109.32 |