Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data
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Updated: Jan 25, 2026

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
Published on: December 9, 2022
Katrin Sippel1, Julia Moser2, Franziska Schleger2
1Eberhard-Karls-University of Tuebingen, Wilhelm-Schickard- Institute for Computer Science - Computer Engineering Department, Tuebingen 72076 Germany; Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich University of Tuebingen, fMEG Center; German Centre for Diabetes Research (DZD), Tuebingen 72076, Germany.
Background And Objective:
Fetal magnetoencephalography (fMEG) is a method for recording fetal brain signals, fetal and maternal heart activity simultaneously. The identification of the R-peaks of the heartbeats forms the basis for later heart rate (HR) and heart rate variability (HRV) analysis. The current procedure for the evaluation of fetal magnetocardiograms (fMCG) is either semi-automated evaluation using template matching (SATM) or Hilbert transformation algorithm (HTA). However, none of the methods available at present works reliable for all datasets.
Methods:
Our aim was to develop a unitary, responsive and fully automated R-peak detection algorithm (FLORA) that combines and enhances both of the methods used up to now.
Results:
The evaluation of all methods on 55 datasets verifies that FLORA outperforms both of these methods as well as a combination of the two, which applies in particular to data of fetuses at earlier gestational age.
Conclusion:
The combined analysis shows that FLORA is capable of providing good, stable and reproducible results without manual intervention.
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