Application of Dynamic Time Warping on Kalman Filtering Framework for Abnormal ECG Filtering

Mohammad Niknazar 1 Bertrand Rivet 1 Christian Jutten 1
1 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
Abstract : Existing nonlinear Bayesian filtering frameworks serve as an effective tool for the model-based filtering of noisy ECG recordings. However, since these methods are based on linear phase assumption, for some heart defects where abnormal waves only appear in certain cycles of the ECG, they are unable to simultaneously filter the normal and abnormal ECG segments. In this paper, a new method based on Dynamic Time Warping (DTW), which benefits information of all channels for nonlinear phase state calculation is presented. Results on real and synthetic data show that the new method can be successfully applied for filtering normal and abnormal ECG segments simultaneously.
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https://hal.archives-ouvertes.fr/hal-00701592
Contributor : Mohammad Niknazar <>
Submitted on : Friday, May 25, 2012 - 4:24:25 PM
Last modification on : Monday, July 8, 2019 - 3:08:34 PM

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  • HAL Id : hal-00701592, version 1

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Mohammad Niknazar, Bertrand Rivet, Christian Jutten. Application of Dynamic Time Warping on Kalman Filtering Framework for Abnormal ECG Filtering. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Apr 2012, Bruges, Belgium. pp.139. ⟨hal-00701592⟩

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