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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/99573

    Title: Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data
    Authors: Weng, Julia Tzu-Ya;Lin, Jyun-Jie;Chen, Yi-Cheng;Chang, Pei-Chann
    Contributors: 淡江大學資訊工程學系
    Keywords: 12-lead ECG;Myocardial infarction;Principal component;Polynomial approximation analysis;Support vector machine
    Date: 2014-05-13
    Issue Date: 2014-11-30 13:06:20 (UTC+8)
    Publisher: Springner
    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%.
    Relation: Lecture Notes in Artificial Intelligence 8643, pp.689-699
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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