Fetal Electrocardiogram R-peak Detection using Robust Tensor Decomposition and Extended Kalman Filtering

Mahsa Akhbari (1, 2) , Mohammad Niknazar (2) , Christian Jutten (2) , Mohammad Bagher Shamsollahi (1) , Bertrand Rivet (2)

1 BiSIPL - Biomedical Signal and Image Processing Laboratory [Teheran]
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

In this paper, we propose an efficient method for R-peak detection in noninvasive fetal electrocardiogram (ECG) signals which are acquired from multiple electrodes on mother's abdomen. The proposed method is performed in two steps: first, we employ a robust tensor decomposition-based method for fetal ECG extraction, assuming different heart rates for mother and fetal ECG; then a method based on extended Kalman filter (EKF) in which the ECG beat is modeled by 3 state equations (P, QRS and T), is used for fetal R-peak detection. The results show that the proposed method is efficiently able to estimate the location of R-peaks of fetal ECG sig-nals. The obtained average scores of event 4 and 5 on the set B of "Physionet Challenge 2013" data are 1326.21 and 45.06, respectively, which are better than the average score for "sample submission physionet2013.m" (avail-able at PhysioNet) on set B which were 3258.56 and 102.75.

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

Mahsa Akhbari, Mohammad Niknazar, Christian Jutten, Mohammad Bagher Shamsollahi, Bertrand Rivet. Fetal Electrocardiogram R-peak Detection using Robust Tensor Decomposition and Extended Kalman Filtering. CinC 2013 - Computing in Cardiology Conference, Sep 2013, Zaragoza, Spain. ⟨hal-01094992⟩