Detection of Atrial Fibrillation and Supraventricular arrhythmia
Date Issued
2014
Date
2014
Author(s)
LIN, Chu-Hsuan
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.
Subjects
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
Type
thesis
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