Hierarchical probabilistic framework for fetal R-peak detection, using ECG waveform and heart rate information

Guy Warmerdam, Rik Vullings, Lars Schmitt, Judith van Laar, Jan W.M. Bergmans

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)
95 Downloads (Pure)

Abstract

The abdominal fetal electrocardiogram (fECG) can provide valuable information about fetal well-being. However, fetal R-peak detection in abdominal fECG recordings is challenging due to the low signal-to-noise ratio (SNR) and the nonstationary nature of the fECG waveform in the abdominal recordings. In this paper, we propose a multichannel hierarchical probabilistic framework for fetal R-peak detection that combines predictive models of the ECG waveform and the heart rate. The performance of our method was evaluated on set-A of the 2013 Physionet/Computing in Cardiology Challenge and compared to the performance of several methods that have been proposed in the literature. The hierarchical probabilistic framework presented in this study outperforms other methods for fetal R-peak detection with a mean overall detection accuracy for set-A of 99.6%. Even for recordings with low SNR our method enables reliable fetal R-peak detection (Ac 99.4%).

Original languageEnglish
Article number8404121
Pages (from-to)4388-4397
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume66
Issue number16
DOIs
Publication statusPublished - 15 Aug 2018

Keywords

  • fetal electrocardiography
  • Hierarchical Bayesian model
  • Kalman filtering
  • R-peak detection

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