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 - GIPSA - Vision and Brain Signal Processing

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.

Mots clés: ECG denoising; Kalman filtering; nonlinear Bayesian filtering; linear phase; dynamic time warping

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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