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گواهی نمایه سازی مقاله Manifold Learning for ECG Arrhythmia Recognition

عنوان مقاله: Manifold Learning for ECG Arrhythmia Recognition
شناسه (COI) مقاله: ICBME20_049
منتشر شده در بیستمین کنفرانس مهندسی زیست پزشکی ایران در سال ۱۳۹۲
مشخصات نویسندگان مقاله:

E Lashgari - Department of Electrical Engineering Sharif University of Technology Tehran, Iran
M Jahed - Department of Electrical Engineering Sharif University of Technology Tehran, Iran
B Khalaj - Department of Electrical Engineering Sharif University of Technology Tehran, Iran

خلاصه مقاله:
Heart is a complex system and we can find its function in electrocardiogram (ECG) signal. The records show high mortality rate of heart diseases. So it is essential to detectand recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECGsignals. Premature Ventricular Contractions (PVC) is a commontype of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detectingPVC and visualizing data with respect to ECG morphologies by using manifold learning. To this end, the Laplacian Eigenmaps –One of the reduction method and it is in the nonlinear category -- is used to extract important dimensions of the ECG signals,followed by the application of Bayesian and FLDA methods for classifying the ECG data. The recognition performance of systemwas evaluated through accuracy, sensitivity and specificitymeasures. The best result shows that 98.97

کلمات کلیدی:
Manifold Learning , Laplacian Eigenmaps , Electrocardiogra , Nonlinear Dimensionality Reduction Methods.

صفحه اختصاصی مقاله و دریافت فایل کامل: https://www.civilica.com/Paper-ICBME20-ICBME20_049.html