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Fiducial points extraction and characteristicwaves detection in ECG signal using a model-based bayesian framework

Akhbari, M ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP.2013.6637852
  3. Publisher: 2013
  4. Abstract:
  5. The automatic detection of Electrocardiogram (ECG) waves is important to cardiac disease diagnosis. A good performance of an automatic ECG analyzing system depends heavily upon the accurate and reliable detection of QRS complex, as well as P and T waves. In this paper, we propose an efficient method for extraction of characteristic points of ECG signal. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was constructed. Quantitative and qualitative evaluations of the proposed method have been done on Physionet QT database (QTDB). This method is also compared with another EKF approach (EKF17). Results show that the proposed method can detect fiducial points of ECG precisely and mean and standard deviation of estimation error do not exceed two samples (8 msec)
  6. Keywords:
  7. Characteristic Waves ; Electrocardiogram (ECG) ; Extended Kalman Filter (EKF) ; Fiducial Point Extraction ; Segmentation ; Automatic Detection ; Bayesian frameworks ; Dynamic parameters ; Extraction of characteristics ; Fiducial points ; Mean and standard deviations ; Qualitative evaluations ; Reliable detection ; Diagnosis ; Estimation ; Extended Kalman filters ; Extraction ; Image segmentation ; Signal detection ; Electrocardiography
  8. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2013 , Pages 1257-1261 ; 15206149 (ISSN) ; 9781479903566 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6637852