A Novel Approach to Track Fetal Movement Using Multi-sensor Magnetocardiographic Recordings

Govindan, Rathinaswamy B.; Ulusar, Umit Deniz; Wilson, James D.; McKelvey, Samantha S.; Preißl, Hubert; Eswaran, Hari

doi:10.1007/s10439-010-0231-z

Cited by 39 publications

(31 citation statements)

References 25 publications

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“…In quiet sleep state the fetus is not moving and the heart rate is stable. The absence of motion was indicated by actocardiogram (Govindan et al, 2010). …”

Section: Resultsmentioning

confidence: 99%

Removal of interference from fetal MEG by frequency dependent subtraction

Vrba¹,

McCubbin²,

Govindan³

et al. 2012

NeuroImage

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Fetal magnetoencephalography (fMEG) recordings are contaminated by maternal and fetal magnetocardiography (MCG) signals and by other biological and environmental interference. Currently, all methods for the attenuation of these signals are based on a time-domain approach. We have developed and tested a frequency dependent procedure for removal of MCG and other interference from the fMEG recordings. The method uses a set of reference channels and performs subtraction of interference in the frequency domain (SUBTR). The interference-free frequency domain signals are converted back to the time domain. We compare the performance of the frequency dependent approach with our present approach for MCG attenuation based on orthogonal projection (OP). SUBTR has an advantage over OP and similar template approaches because it removes not only the MCG but also other small amplitude biological interference, avoids the difficulties with inaccurate determination of the OP operator, provides more consistent and stable fMEG results, does not cause signal redistribution, and if references are selected judiciously, it does not reduce fMEG signal amplitude. SUBTR was found to perform well in simulations and on real fMEG recordings, and has a potential to improve the detection of fetal brain signals. The SUBTR removes interference without the need for a model of the individual interference sources. The method may be of interest for any sensor array noise reduction application where signal-free reference channels are available.

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“…In quiet sleep state the fetus is not moving and the heart rate is stable. The absence of motion was indicated by actocardiogram (Govindan et al, 2010). …”

Section: Resultsmentioning

confidence: 99%

Removal of interference from fetal MEG by frequency dependent subtraction

Vrba¹,

McCubbin²,

Govindan³

et al. 2012

NeuroImage

Self Cite

View full text Add to dashboard Cite

show abstract

“…(12, 16) Briefly, after the data were bandpass filtered between 0.5–70Hz, and semi-automated artifact rejection applied, the R-wave was identified using adaptive Hilbert transform approach (31, 32). For spectral analysis the RRi was converted into evenly sampled data using cubic-spline interpolation at a sample rate of 4 Hz.…”

Section: Methodsmentioning

confidence: 99%

Effect of Temperature on Heart Rate Variability in Neonatal ICU Patients With Hypoxic-Ischemic Encephalopathy

Massaro

Campbell

Metzler

et al. 2017

Pediatric Critical Care Medicine

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Objective To determine whether measures of heart rate variability (HRV) are related to changes in temperature during rewarming after therapeutic hypothermia (TH) for hypoxic ischemic encephalopathy (HIE). Design Prospective observational study. Setting Level 4 neonatal intensive care unit in a free-standing academic children's hospital. Patients Forty-four infants with moderate to severe HIE treated with TH. Interventions Continuous EKG data from 2 hours prior to rewarming through 2 hours after completion of rewarming (up to 10 hours) were analyzed. Measurements and Main Results Median beat-to-beat interval (RRi) and measures of HRV were quantified including RRi standard deviation (SD), low (LF) and high (HF) frequency relative spectral power, detrended fluctuation analysis short- and long- α exponents (αS, αL) and root mean square short- and long- time scales (RMSS, RMSL). The relationships between HRV measures and esophageal/axillary temperatures were evaluated. HRV measures LF, αS, RMSS, and RMSL were negatively associated, while αL was positively associated, with temperature (P<0.01). These findings signify an overall decrease in HRV as temperature increased towards normothermia. Conclusions Measures of HRV are temperature dependent in the range of TH to normothermia. Core body temperature needs to be considered when evaluating HRV metrics as potential physiological biomarkers of illness severity in HIE infants undergoing TH.

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“…Additionally, the final report of fCTI analysis shows a 30-min fetal heart rate trend graph, fetal movements [8], entropy of fetal heart pattern [7], a histogram of R-peak amplitudes, and a short single fetal heart tracing ( Figure 4). …”

Section: Data Processingmentioning

confidence: 99%