Abstract
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.
Highlights
Fetal magnetoencephalography facilitates the investigation of fetal brain and autonomic nervous system development [1, 2]
Number of detected peaks per minute (NP) A comparison of the number of heart beats (NP) per minute as obtained with the four different methods showed a significant difference between semiautomated evaluation using template matching (SATM) [132.67 ± 36.86] and fully automated R-peak detection algorithm (FLORA) [139.17 ± 23.15] as well as between Hilbert transformation algorithm (HTA) [137.40 ± 235 41.05] and FLORA
Difference between RR measures (RR-DIFF) The difference between RRmain and the mean RR-interval RRest of the estimated heart rate (HR) showed a significant difference between SATM [0.01 ± 1.57] 240 and FLORA [0.001 ± 0.72] as well as HTA [0.005 ± 1.96] and FLORA
Summary
Fetal magnetoencephalography (fMEG) facilitates the investigation of fetal brain and autonomic nervous system development [1, 2]. With its good spatio-temporal resolution, it allows to monitor maternal (mMCG) and fetal magnetocardiograms (fMCG) simultaneously to the recording of fetal brain activity (see Fig. 2) This enables us to evaluate maternal 10 and fetal heart rate (HR), different parameters of heart rate variability (HRV) [12] and fetal behavioural states [13, 14], starting at 20 weeks of gestational age. A combination of semi automated evaluation by experts using template matching (SATM) and the automated Hilbert transformation approach (HTA) [16] is the standard procedure for both maternal and fetal R-peak detection. Both 25 of these commonly used methods function for most maternal heart evaluations since the representation of the maternal heart signal is quite strong and virtually stationary. Conclusion: The combined analysis shows that FLORA is capable of providing
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