Search for: premature-ventricular-contraction
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    Manifold learning for ECG arrhythmia recognition

    , Article 2013 20th Iranian Conference on Biomedical Engineering, ICBME 2013 ; 2013 , Pages 126-131 Lashgari, E ; Jahed, M ; Khalaj, B ; Sharif University of Technology
    IEEE Computer Society  2013
    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 detect and recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECG signals. Premature Ventricular Contractions (PVC) is a common type of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detecting PVC 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... 

    A trainable neural network ensemble for ECG beat classification

    , Article World Academy of Science, Engineering and Technology ; Volume 70 , 2010 , Pages 788-794 ; 2010376X (ISSN) Sajedin, A ; Zakernejad, S ; Faridi, S ; Javadi, M ; Ebrahimpour, R ; Sharif University of Technology
    This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then...