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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.
Criteria of the automatic state classification based on the original Nijhuis criteria.
State/fHRP | 1F/fHRP1 quiet sleep | 2F/fHRP2 active sleep | 4F/fHRP4 active awake |
---|---|---|---|
Original criteria | • Quiescence which can be regularly interrupted by brief body movements (startles) |
• Frequent gross body movement |
• Vigorous activity with many trunk rotations |
Baseline | <160 bpm | <160 bpm | >160 bpm possible |
Oscillation bandwith | <±7.5 bpm | ±7.5–±15 bpm | >±15 bpm |
Accelerations | No | >15 bpm/>15 s | >30 bpm/>30 s |
Movement | No | Yes | Yes |
Fetal magnetocardiography uses SQUID biomagnetometry to non-invasively record fetal heart function through the maternal abdomen. This method detects the fetal cardio electrophysiology with high temporal resolution (1 ms) superior to CTG and is less susceptible to artifacts than fECG (
The focus of this study was the inclusion of fetal movement data according to the original Nijhuis criteria for fetal state classification. Additionally, HRV parameters (SDNN, RMSSD, ratio SDNN/RMSSD, PE) were simultaneously studied to gain information about the developing influence of the ANS within each fetal state. This was done by adapting the design of an earlier fMCG study (
The study was performed with the fetal biomagnetometer installed at the fMEG Center in Tuebingen, Germany (
A CTG was routinely performed before every fMCG recording to confirm fetal heart rate and activity as normal for GA. Furthermore, the fetal position in relation to the sensor array was checked by ultrasound (Logiq 500 MD, GE Healthcare, Little Chalfont, Buckinghamshire, UK) prior to the recording. The study was performed in a magnetically shielded room (Vakuumschmelze, Hanau, Germany). The sensor array consisted of 156 SQUIDs (first order gradiometers) and 29 reference channels for noise detection (CTF MEGTM System, VSM Med. Tech, Coquitlam, BC, Canada). Subjects were seated comfortably in an upright position and asked to lean forward against the concave sensor array, modeled especially for the pregnant abdomen. Four coils fixed on elastic belts were positioned around the maternal abdomen to mark fetal head position with respect to the sensor array and to detect maternal movement during the measurement. The mothers were asked to relax during the recording and to move as little as possible. A choice of relaxing music was offered and transferred via air-conducting lines from a music player outside the room to a headphone. The duration of the recording depended on maternal comfort and was set to a maximum of 45 min. Subsequently, the ultrasound examination was repeated to check fetal position.
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 (
Statistics were performed with SPSS 18.0 for Windows (IBM, Armonk, NY, USA). A one-way ANOVA was used for the statistical analysis of fetal behavioral states (independent variable) and parameters of HRV (dependent variable;
Starting at 24 weeks of GA, we performed measurements in 55 pregnant women (mean age 33 years) with a mean recording time of 32.5 min (range 10–45 min) divided into three groups by GA (group 1,
Mean heart rate was stable between group 1 (144 bpm) and group 2 (145 bpm), but decreased to 141 bpm in group 3 (corr: -0.363,
The SDNN showed an increasing trend with GA for state 2F and 4F, as seen in
The RMSSD showed no clear decrease or increase across the GA groups, nor was the correlation between RMSSD and GA statistically significant (corr: 0.103;
The SDNN/RMSSD ratio (
PE did not show any significant changes between the GA groups (corr: 0.179;
Group | mHR | SDNN | RMSSD | SDNN/RMSSD ratio | PE |
---|---|---|---|---|---|
1–2 |
0.032 ( |
0.217 ( |
0.041 ( |
0.165 ( |
|
Distribution of the measured parameters of fHRP divided in groups regarding GA [Mean (STD)].
Group 1 | Rest: | Active: | |
---|---|---|---|
Mean HR | 142.76 (5.10) | 148.72 (7.36) | |
SDNN | 7.48 (0.82) | 18.77 (4.38) | |
RMSSD | 3.23 (1.13) | 13.15 (9.78) | |
Ratio SDNN/RMSSD | 2.55 (0.82) | 1.99 (1.26) | |
Perm. entropy | 0.93 (0.03) | 0.92 (0.03) | |
Mean HR | 136.34 (6.80) | 141.02 (6.77) | 156.92 (10.63) |
SDNN | 8.84 (1.22) | 22.59 (3.93) | 26.99 (10.39) |
RMSSD | 4.57 (1.44) | 7.82 (3.57) | 6.99 (2.04) |
Ratio SDNN/RMSSD | 2.10 (0.58) | 3.33 (1.04) | 4.06 (1.17) |
Perm. Entropy | 0.95 (0.02) | 0.94 (0.02) | 0.93 (0.03) |
Mean HR | 134.21 (7.45) | 137.83 (6.03) | 150.82 (4.97) |
SDNN | 7.43 (1.57) | 25.43 (4.87) | 34.04 (10.83) |
RMSSD | 4.29 (1.14) | 8.83 (3.46) | 7.29 (4.55) |
Ratio SDNN/RMSSD | 1.84 (0.59) | 3.28 (1.28) | 5.41 (2.21) |
PE | 0.95 (0.02) | 0.94 (0.02) | 0.90 (0.03) |
Classical fHRV parameters were calculated for each recording in relation to the different fetal behavioral states.
State changes | mHR | SDNN | RMSSD | SDNN/RMSSD ratio | PE |
---|---|---|---|---|---|
1F–2F | 0.057 ( |
0.721 ( |
|||
1F–4F | |||||
2F–4F | 0.030 ( |
0.713 ( |
0.042 ( |
Behavioral states in mature normal fetuses were primarily investigated by ultrasound relating to the original Nijhuis criteria, namely fHRPs, eye movement, and general body movement (
During early gestation, only quiet vs. active states were distinguishable, representing the premature fetus. With progressing gestation, heart rate patterns became more defined, and matched fetal movement. The frequencies of fetal behavioral states developed as expected (
Our further goal was to assess neurovegetative modulation by comparing established parameters of fetal HRV, namely SDNN, RMSSD, SDNN/RMSSD ratio, and PE, with the fetal behavioral states 1F, 2F, and 4F across three GA groups. The total values of the HRV parameters were in accordance with an MCG study based on visual classification of fHRPs to identify behavioral states (
The fHRV parameters may help to differentiate between fetal behavioral states and indicate the neurovegetative modulation within each state, thus offering greater insight into the vegetative development in utero. This confirms other studies pointing to the SDNN as a distinguishing parameter (
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We thank Magdalene Weiss from the fMEG-Center Tuebingen for her substantial work for this study. The study was approved by the Ethics Committee of the University of Tuebingen (No.476/2008MPG1). This study was supported by the Deutsche Forschungsgemeinschaft (DFG BI 195/50 and KI 1306/3-1), The University of Tuebingen (E.05.00303 and E.05.0259.1), and the Landesstiftung Baden-Wuerttemberg, Germany.
autonomic nervous system
beats per minute
cardiotocogram, cardiotocography, fECG, fetal Electrocardiography
fetal heart rate variability
fetal heart rate pattern
fetal magnetocardiography
gestational age
heart rate variability
intrauterine growth restriction
mean heart rate
permutation entropy
root mean square of successive differences of the R–R intervals
standard deviation of the normal–to-normal R–R intervals
standard deviation of the normal–to–normal R–R intervals