کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
727784 892803 2010 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Detection of premature ventricular contractions using MLP neural networks: A comparative study
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
Detection of premature ventricular contractions using MLP neural networks: A comparative study
چکیده انگلیسی

This paper proposes a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising module, a feature extraction module and a classification module. In the first module we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. The feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptron (MLP) neural networks with different number of layers and nine training algorithms are designed. The performances of the networks for speed of convergence and accuracy classifications are evaluated for seven files from the MIT–BIH arrhythmia database. Among the different training algorithms, the resilient back-propagation (RP) algorithm illustrated the best convergence rate and the Levenberg–Marquardt (LM) algorithm achieved the best overall detection accuracy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 43, Issue 1, January 2010, Pages 103–112
نویسندگان
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