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Heart rate variability parameters and fetal movement complement fetal behavioral states detection via magnetography to monitor neurovegetative development.

Brändle J, Preissl H, Draganova R, Ortiz E, Kagan KO, Abele H, Brucker SY, Kiefer-Schmidt I - Front Hum Neurosci (2015)

Bottom Line: SDNN increased over gestation.Changes of HRV parameters between the fetal behavioral states, especially between 1F and 4F, were statistically significant.Increasing fetal activity was confirmed by a decrease in PE complexity measures.

View Article: PubMed Central - PubMed

Affiliation: University Women's Hospital and Research Institute for Women's Health, University of Tuebingen Tuebingen, Germany ; fMEG Center, University of Tuebingen Tuebingen, Germany ; Department of Obstetrics and Gynecology, University of Tuebingen Tuebingen, Germany.

ABSTRACT
Fetal behavioral states are defined by fetal movement and heart rate variability (HRV). At 32 weeks of gestational age (GA) the distinction of four fetal behavioral states represented by combinations of quiet or active sleep or awakeness is possible. Prior to 32 weeks, only periods of fetal activity and quiesence can be distinguished. The increasing synchronization of fetal movement and HRV reflects the development of the autonomic nervous system (ANS) control. Fetal magnetocardiography (fMCG) detects fetal heart activity at high temporal resolution, enabling the calculation of HRV parameters. This study combined the criteria of fetal movement with the HRV analysis to complete the criteria for fetal state detection. HRV parameters were calculated including the standard deviation of the normal-to-normal R-R interval (SDNN), the mean square of successive differences of the R-R intervals (RMSSD, SDNN/RMSSD ratio, and permutation entropy (PE) to gain information about the developing influence of the ANS within each fetal state. In this study, 55 magnetocardiograms from healthy fetuses of 24-41 weeks' GA were recorded for up to 45 min using a fetal biomagnetometer. Fetal states were classified based on HRV and movement detection. HRV parameters were calculated for each state. Before GA 32 weeks, 58.4% quiescence and 41.6% activity cycles were observed. Later, 24% quiet sleep state (1F), 65.4% active sleep state (2F), and 10.6% active awake state (4F) were observed. SDNN increased over gestation. Changes of HRV parameters between the fetal behavioral states, especially between 1F and 4F, were statistically significant. Increasing fetal activity was confirmed by a decrease in PE complexity measures. The fHRV parameters support the differentiation between states and indicate the development of autonomous nervous control of heart rate function.

No MeSH data available.


Related in: MedlinePlus

Example of an actocardiogram in 38 weeks of GA measured over 45 min (first line: cardiogram in bpm; second line: actogram).
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Figure 1: Example of an actocardiogram in 38 weeks of GA measured over 45 min (first line: cardiogram in bpm; second line: actogram).

Mentions: The recordings were performed at a sampling rate of 1220.7 Hz. Datasets with low signal-to-noise ratios for fetal heart signals and data with more than 3% artifacts or missed heartbeats were excluded from the analysis. All data were filtered with a bandpass of 1–80 Hz using the 8th order Butterworth filter with zero-phase distortion. Maternal heart signals were attenuated using a signal space projection technique and the fetal R-waves were identified using the Hilbert transform technique. The time between two R-waves was defined as a beat-to-beat interval and used to calculate fetal mHR. Classical parameters of fHRV representing the time domain (SDNN, RMSSD, SDNN/RMSSD ratio) and a non-linear fHRV measure (PE) were calculated for each state, 1F through 4F, and each gestational group in a moving window of 256 bpm. As a preprocessing step, a shifting window with a fixed size of 256 heartbeats was standardized in accordance with recommended standards (Malik, 1996; Grimm et al., 2003). The HRV parameters calculated were: SDNN – the standard deviation of normal-to-normal beats – representing the overall variability of sympathetic and vagal oscillations in the short data windows; RMSSD – the root mean square of successive differences, reflecting vagal control; the SDNN/RMSSD ratio – relating overall variability to its short-term variability shared in the time domain as a measure of sympathovagal balance (Schneider et al., 2008); and PE, representing the complexity of heart rate series (Frank et al., 2006). Fetal heart rate over time was plotted in bpm as a cardiogram in a CTG-like fashion. The fMCG signal measures fetal movement as changes in the orientation of fetal heart vectors with respect to the sensor array. This detection of fetal movement is orientated solely on the fetal heart vector and therefore only gross fetal movements such as trunk rotations are discernible. The resulting variation in signal amplitude was plotted as an actogram showing the fluctuation of the baseline over time. Any deviation >25% from baseline was considered to represent fetal movement. The cardiogram and the actogram were recorded simultaneously and plotted together as an actocardiogram, plotted in Figure 1. We developed an algorithm for automatic state classification based on the Nijhuis criteria (Table 1), taking into account the occurrence of fetal movement and the fHRP. All datasets were additionally classified by visual inspection of the actocardiograms by an observer with experience in the analysis of CTG and actocardiograms. A second observer with experience further independently analyzed the actocardiograms. If disagreement occurred, consensus was achieved by revision. Due to the low occurrence of the 3F state (Schneider et al., 2008), only the 1F, 2F, and 4F states were included in the present analysis. Prior to GA 32 weeks (group 1), only active and quiet states were distinguished, corresponding to the algorithm criteria of 1F for quiescence and 2F for activity (Pillai and James, 1990b).


Heart rate variability parameters and fetal movement complement fetal behavioral states detection via magnetography to monitor neurovegetative development.

Brändle J, Preissl H, Draganova R, Ortiz E, Kagan KO, Abele H, Brucker SY, Kiefer-Schmidt I - Front Hum Neurosci (2015)

Example of an actocardiogram in 38 weeks of GA measured over 45 min (first line: cardiogram in bpm; second line: actogram).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388008&req=5

Figure 1: Example of an actocardiogram in 38 weeks of GA measured over 45 min (first line: cardiogram in bpm; second line: actogram).
Mentions: The recordings were performed at a sampling rate of 1220.7 Hz. Datasets with low signal-to-noise ratios for fetal heart signals and data with more than 3% artifacts or missed heartbeats were excluded from the analysis. All data were filtered with a bandpass of 1–80 Hz using the 8th order Butterworth filter with zero-phase distortion. Maternal heart signals were attenuated using a signal space projection technique and the fetal R-waves were identified using the Hilbert transform technique. The time between two R-waves was defined as a beat-to-beat interval and used to calculate fetal mHR. Classical parameters of fHRV representing the time domain (SDNN, RMSSD, SDNN/RMSSD ratio) and a non-linear fHRV measure (PE) were calculated for each state, 1F through 4F, and each gestational group in a moving window of 256 bpm. As a preprocessing step, a shifting window with a fixed size of 256 heartbeats was standardized in accordance with recommended standards (Malik, 1996; Grimm et al., 2003). The HRV parameters calculated were: SDNN – the standard deviation of normal-to-normal beats – representing the overall variability of sympathetic and vagal oscillations in the short data windows; RMSSD – the root mean square of successive differences, reflecting vagal control; the SDNN/RMSSD ratio – relating overall variability to its short-term variability shared in the time domain as a measure of sympathovagal balance (Schneider et al., 2008); and PE, representing the complexity of heart rate series (Frank et al., 2006). Fetal heart rate over time was plotted in bpm as a cardiogram in a CTG-like fashion. The fMCG signal measures fetal movement as changes in the orientation of fetal heart vectors with respect to the sensor array. This detection of fetal movement is orientated solely on the fetal heart vector and therefore only gross fetal movements such as trunk rotations are discernible. The resulting variation in signal amplitude was plotted as an actogram showing the fluctuation of the baseline over time. Any deviation >25% from baseline was considered to represent fetal movement. The cardiogram and the actogram were recorded simultaneously and plotted together as an actocardiogram, plotted in Figure 1. We developed an algorithm for automatic state classification based on the Nijhuis criteria (Table 1), taking into account the occurrence of fetal movement and the fHRP. All datasets were additionally classified by visual inspection of the actocardiograms by an observer with experience in the analysis of CTG and actocardiograms. A second observer with experience further independently analyzed the actocardiograms. If disagreement occurred, consensus was achieved by revision. Due to the low occurrence of the 3F state (Schneider et al., 2008), only the 1F, 2F, and 4F states were included in the present analysis. Prior to GA 32 weeks (group 1), only active and quiet states were distinguished, corresponding to the algorithm criteria of 1F for quiescence and 2F for activity (Pillai and James, 1990b).

Bottom Line: SDNN increased over gestation.Changes of HRV parameters between the fetal behavioral states, especially between 1F and 4F, were statistically significant.Increasing fetal activity was confirmed by a decrease in PE complexity measures.

View Article: PubMed Central - PubMed

Affiliation: University Women's Hospital and Research Institute for Women's Health, University of Tuebingen Tuebingen, Germany ; fMEG Center, University of Tuebingen Tuebingen, Germany ; Department of Obstetrics and Gynecology, University of Tuebingen Tuebingen, Germany.

ABSTRACT
Fetal behavioral states are defined by fetal movement and heart rate variability (HRV). At 32 weeks of gestational age (GA) the distinction of four fetal behavioral states represented by combinations of quiet or active sleep or awakeness is possible. Prior to 32 weeks, only periods of fetal activity and quiesence can be distinguished. The increasing synchronization of fetal movement and HRV reflects the development of the autonomic nervous system (ANS) control. Fetal magnetocardiography (fMCG) detects fetal heart activity at high temporal resolution, enabling the calculation of HRV parameters. This study combined the criteria of fetal movement with the HRV analysis to complete the criteria for fetal state detection. HRV parameters were calculated including the standard deviation of the normal-to-normal R-R interval (SDNN), the mean square of successive differences of the R-R intervals (RMSSD, SDNN/RMSSD ratio, and permutation entropy (PE) to gain information about the developing influence of the ANS within each fetal state. In this study, 55 magnetocardiograms from healthy fetuses of 24-41 weeks' GA were recorded for up to 45 min using a fetal biomagnetometer. Fetal states were classified based on HRV and movement detection. HRV parameters were calculated for each state. Before GA 32 weeks, 58.4% quiescence and 41.6% activity cycles were observed. Later, 24% quiet sleep state (1F), 65.4% active sleep state (2F), and 10.6% active awake state (4F) were observed. SDNN increased over gestation. Changes of HRV parameters between the fetal behavioral states, especially between 1F and 4F, were statistically significant. Increasing fetal activity was confirmed by a decrease in PE complexity measures. The fHRV parameters support the differentiation between states and indicate the development of autonomous nervous control of heart rate function.

No MeSH data available.


Related in: MedlinePlus