Fetal autonomic response to severe acidaemia during labour.

OBJECTIVE: Spectral analysis of heart-rate variability is used to monitor autonomic nervous system fluctuations. The low-frequency component is associated with sympathetic and parasympathetic modulation and the high-frequency component is associated with parasympathetic modulation. The objective was to study whether changes in low-frequency or high-frequency power of heart-rate variability occur in case of fetal distress. DESIGN: Case-control study. SETTING: Obstetric unit of a tertiary-care teaching hospital. POPULATION: Twenty healthy human fetuses during labour at term of which ten had an umbilical artery pH < 7.05 (cases), and ten had an arterial pH > 7.20 (controls) after birth. METHODS: Spectral information about fetal beat-to-beat heart rate, calculated from direct fetal electrocardiogram registrations, was obtained by using a short-time Fourier transform. MAIN OUTCOME MEASURES: Absolute power and normalised power in the low-frequency and high-frequency bands. RESULTS: No differences were found between fetuses with and without acidaemia in absolute low or high frequency power (P = 0.2 and P = 0.3, respectively). During the last 30 minutes of labour, acidaemic fetuses had significantly increased normalised low-frequency power (P = 0.01) and decreased normalised high-frequency power (P = 0.03) compared with non-acidaemic fetuses. These differences were not observed from 3 to 2 hours before birth (P = 0.7 and P = 0.9, respectively). CONCLUSION: The autonomic nervous system of human fetuses at term responds adequately to severe stress during labour. Normalised low and high frequency power of heart-rate variability might be able to discriminate between normal and abnormal fetal condition.

Dynamic segmentation and linear prediction for maternal ECG removal in antenatal abdominal recordings.

Monitoring the fetal heart rate (fHR) and fetal electrocardiogram (fECG) during pregnancy is important to support medical decision making. Before labor, the fHR is usually monitored using Doppler ultrasound. This method is inaccurate and therefore of limited clinical value. During labor, the fHR can be monitored more accurately using an invasive electrode; this method also enables monitoring of the fECG. Antenatally, the fECG and fHR can also be monitored using electrodes on the maternal abdomen. The signal-to-noise ratio of these recordings is, however, low, the maternal electrocardiogram (mECG) being the main interference. Existing techniques to remove the mECG from these non-invasive recordings are insufficiently accurate or do not provide all spatial information of the fECG. In this paper a new technique for mECG removal in antenatal abdominal recordings is presented. This technique operates by the linear prediction of each separate wave in the mECG. Its performance in mECG removal and fHR detection is evaluated by comparison with spatial filtering, adaptive filtering, template subtraction and independent component analysis techniques. The new technique outperforms the other techniques in both mECG removal and fHR detection (by more than 3%).

Spectral analysis of fetal heart rate variability for fetal surveillance: review of the literature.

BACKGROUND: Cardiotocography has a poor diagnostic value in detecting fetal acidosis. Spectral analysis of fetal heart rate variability can be used to monitor the fetal autonomic nervous system. OBJECTIVE: To determine the value of spectral analysis for fetal surveillance. METHODS: A systematic search was performed in the electronic databases CENTRAL (the Cochrane Library; 2007, Issue 3), PUBMED and EMBASE up to May 2007. Articles that described spectral analysis of human fetal heart rate variability and compared the energy in spectral bands with blood-gas values obtained by funipuncture or from the umbilical cord immediately postpartum were included. RESULTS: Six studies met the inclusion criteria. The included studies were heterogeneous, various methods of spectral analysis and different frequency bands were used and the outcome measures varied. Five out of six studies showed a decrease in spectral energy in the low frequency (LF) band in case of fetal distress. An extremely low LF power had a sensitivity of 97.5% and a specificity of 86.1% to detect fetal distress. CONCLUSIONS: Spectral analysis could be a promising method for fetal surveillance. Larger prospective studies are needed to determine the exact diagnostic value of spectral analysis. For further research, standardisation of spectral analysis is recommended. Studies should focus on real time monitoring.

Beat-to-beat heart rate detection in multi-lead abdominal fetal ECG recordings.

Reliable monitoring of fetal condition often requires more information than is provided by cardiotocography, the standard technique for fetal monitoring. Abdominal recording of the fetal electrocardiogram may offer valuable additional information, but unfortunately is troubled by poor signal-to-noise ratios during certain parts of pregnancy. To increase the usability of abdominal fetal ECG recordings, an algorithm was developed that enhances fetal QRS complexes in these recordings and thereby provides a promising method for detecting the beat-to-beat fetal heart rate in recordings with poor signal-to-noise ratios. The method was evaluated on generated recordings with controlled signal-to-noise ratios and on actual recordings that were performed in clinical practice and were annotated by two independent experts. The evaluation on the generated signals demonstrated excellent results (sensitivity of 0.98 for SNR≥1.5). Only for SNR<2, the inaccuracy of the fetal heart rate detection exceeded 2 ms, which may still suffice for cardiotocography but is unacceptable for analysis of the beat-to-beat fetal heart rate variability. The sensitivity and positive predictive value of the method in actual recordings were reduced to approximately 90% for SNR≤2.4, but were excellent for higher signal-to-noise ratios.

Normalized spectral power of fetal heart rate variability is associated with fetal scalp blood pH.

BACKGROUND: Spectral power of fetal heart rate variability is related to fetal condition. Previous studies found an increased normalized low frequency power in case of severe fetal acidosis. AIMS: To analyze whether absolute or normalized low or high frequency power of fetal heart rate variability is associated with fetal scalp blood pH. STUDY DESIGN: Prospective cohort study, performed in an obstetric unit of a tertiary care teaching hospital. SUBJECTS: Consecutive singleton term fetuses in cephalic presentation that underwent one or more scalp blood samples, monitored during labour using ST-analysis of the fetal electrocardiogram. Ten-minute continuous beat-to-beat fetal heart rate segments, preceding the scalp blood measurement were used. OUTCOME MEASURES: Absolute and normalized spectral power in the low frequency band (0.04-0.15 Hz) and in the high frequency band (0.4-1.5 Hz). RESULTS: In total 39 fetal blood samples from 30 patients were studied. We found that normalized low frequency and normalized high frequency power of fetal heart rate variability is associated with fetal scalp blood pH. The estimated ß of normalized low frequency power was -0.37 (95% confidence interval -0.68 to -0.06) and the relative risk was 0.69 (95% confidence interval 0.51-0.94). The estimated ß of normalized high frequency power was 0.33 (95% confidence interval 0.01-0.65) and the relative risk was 1.39 (95% confidence interval 1.01-1.92). CONCLUSIONS: Normalized low and normalized high frequency power of fetal heart rate variability is associated with fetal scalp blood pH.

A continuous wavelet transform-based method for time-frequency analysis of artefact-corrected heart rate variability data.

Time-frequency analysis of heart rate variability (HRV) provides relevant clinical information. However, time-frequency analysis is very sensitive to artefacts. Artefacts that are present in heart rate recordings may be corrected, but this reduces the variability in the signal and therefore adversely affects the accuracy of calculated spectral estimates. To overcome this limitation of traditional techniques for time-frequency analysis, a new continuous wavelet transform (CWT)-based method was developed in which parts of the scalogram that have been affected by artefact correction are excluded from power calculations. The method was evaluated by simulating artefact correction on HRV data that were originally free of artefacts. Commonly used spectral HRV parameters were calculated by the developed method and by the short-time Fourier transform (STFT), which was used as a reference. Except for the powers in the very low-frequency and low-frequency (LF) bands, powers calculated by the STFT proved to be extremely sensitive to artefact correction. The CWT-based calculations in the high-frequency and very high-frequency bands corresponded well with their theoretical values. The standard deviations of these powers, however, increase with the number of corrected artefacts which is the result of the non-stationarity of the R-R interval series that were analysed. The powers calculated in the LF band turned out to be slightly sensitive to artefact correction, but the results were acceptable up to 20% artefact correction. Therefore, the CWT-based method provides a valuable alternative for the analysis of HRV data that cannot be guaranteed to be free of artefacts.

Quantitative analysis of maturational changes in EEG background activity in very preterm infants with a normal neurodevelopment at 1 year of age.

BACKGROUND: The electroencephalographic (EEG) background pattern of preterm infants changes with postmenstrual age (PMA) from discontinuous activity to continuous activity. However, changes in discontinuity have been investigated by visual analysis only. AIM: To investigate the maturational changes in EEG discontinuity in healthy preterm infants using an automated EEG detection algorithm. STUDY DESIGN: Weekly 4h EEG recordings were performed in preterm infants with a gestational age (GA)<32weeks and normal neurological follow-up at 1year. The channel C3-C4 was analyzed using an algorithm which automatically detects periods of EEG inactivity (interburst intervals). The interburst-burst ratio (IBR, percentage of EEG inactivity during a moving time window of 600s) and mean length of the interburst intervals were calculated. Using the IBR, discontinuous background activity (periods with high IBR) and continuous background activity (periods with low IBR) were automatically detected and their mean length during each recording was calculated. Data were analyzed with regression and multivariate analysis. RESULTS: 79 recordings were performed in 18 infants. All recordings showed a cyclical pattern in EEG discontinuity. With advancing PMA, IBR (R(2)=0.64; p<0.001), interburst interval length (R(2)=0.43; p<0.001) and length of discontinuous activity (R(2)=0.38; p<0.001) decreased, while continuous activity increased (R(2)=0.50; p<0.001). Multivariate analysis showed that all EEG discontinuity parameters were equally influenced by GA and postnatal age. CONCLUSION: Analyzing EEG background activity in preterm infants is feasible with an automated algorithm and shows maturational changes of several EEG derived parameters. The cyclical pattern in IBR suggests brain organisation in preterm infant.

A robust physiology-based source separation method for QRS detection in low amplitude fetal ECG recordings.

The use of the non-invasively obtained fetal electrocardiogram (ECG) in fetal monitoring is complicated by the low signal-to-noise ratio (SNR) of ECG signals. Even after removal of the predominant interference (i.e. the maternal ECG), the SNR is generally too low for medical diagnostics, and hence additional signal processing is still required. To this end, several methods for exploiting the spatial correlation of multi-channel fetal ECG recordings from the maternal abdomen have been proposed in the literature, of which principal component analysis (PCA) and independent component analysis (ICA) are the most prominent. Both PCA and ICA, however, suffer from the drawback that they are blind source separation (BSS) techniques and as such suboptimum in that they do not consider a priori knowledge on the abdominal electrode configuration and fetal heart activity. In this paper we propose a source separation technique that is based on the physiology of the fetal heart and on the knowledge of the electrode configuration. This technique operates by calculating the spatial fetal vectorcardiogram (VCG) and approximating the VCG for several overlayed heartbeats by an ellipse. By subsequently projecting the VCG onto the long axis of this ellipse, a source signal of the fetal ECG can be obtained. To evaluate the developed technique, its performance is compared to that of both PCA and ICA and to that of augmented versions of these techniques (aPCA and aICA; PCA and ICA applied on preprocessed signals) in generating a fetal ECG source signal with enhanced SNR that can be used to detect fetal QRS complexes. The evaluation shows that the developed source separation technique performs slightly better than aPCA and aICA and outperforms PCA and ICA and has the main advantage that, with respect to aPCA/PCA and aICA/ICA, it performs more robustly. This advantage renders it favorable for employment in automated, real-time fetal monitoring applications.

Quantitative analysis of amplitude-integrated electroencephalogram patterns in stable preterm infants, with normal neurological development at one year.

BACKGROUND: The amplitude-integrated EEG (aEEG) is feasible for monitoring cerebral activity in preterm infants. However, quantitative data on normal patterns in these infants are limited. OBJECTIVE: To study maturational aEEG changes in a cohort of stable preterm infants by automated quantification. METHODS: In a cohort of stable preterm infants with gestational age (GA) <32 weeks and normal neurological follow-up at 1 year, weekly 4 h EEG recordings were performed. aEEG traces were obtained from channel C(3)-C(4). The upper margin amplitude (UMA), lower margin amplitude (LMA) and bandwidth (BW) were quantitatively calculated using an expert software system. In addition, the relative duration of discontinuous background pattern (discontinuous background defined as activity with LMA <5 microV, expressed as DC-%) was calculated. RESULTS: 79 aEEG recordings (4-6 recordings/infant) were obtained in 18 infants. Analysis of the first week recordings demonstrated a strong positive correlation between GA and LMA, while DC-% decreased significantly. Longitudinally, all infants showed increase of LMA. Multivariate analysis showed that GA and postnatal age (PA) both contributed independently and equally to LMA and DC-%. We found a strong correlation between postmenstrual age (GA + PA) and LMA and DC-%, respectively. CONCLUSION: To our knowledge, this is the first study where aEEG development was studied by automated quantification of aEEG characteristics in a cohort of stable preterm infants with a normal neurological development at 1 year of age. LMA and DC-% are simple quantitative measures of neurophysiologic development and may be used to evaluate neurodevelopment in infants.

Power spectrum analysis of fetal heart rate variability at near term and post term gestation during active sleep and quiet sleep.

BACKGROUND: Spectral analysis of fetal heart rate variability is promising for assessing fetal condition. Before using spectral analysis for fetal monitoring it has to be determined whether there should be a correction for gestational age or behavioural state. AIMS: Compare spectral values of heart rate variability between near term and post term fetuses during active and quiet sleep. STUDY DESIGN: Case-control. Cases had a gestational age of > or =42 weeks; controls were 36 to 37 weeks. Fetuses were matched for birth weight percentile. SUBJECTS: STAN registrations from healthy fetuses. For each fetus one 5-minute segment was selected during active and one during quiet sleep. OUTCOME MEASURES: Absolute and normalized low (0.04-0.15 Hz) and high frequency power (0.4-1.5 Hz) of heart rate variability. RESULTS: Twenty fetuses were included. No significant differences were found between cases and controls in absolute (481 and 429 respectively, P=0.88) or normalized low (0.78 and 0.80 respectively, P=0.50) or absolute (41 and 21 respectively, P=0.23) or normalized high frequency power (0.08 and 0.07 respectively, P=0.20) during active state. During rest, normalized low frequency power was lower (0.58 and 0.69 respectively, P=0.03) and absolute (16 and 10 respectively, P=0.04) and normalized high frequency power were higher (0.21 and 0.14 respectively, P=0.01) in cases compared to controls. Absolute and normalized low frequency power were higher during active state compared to rest in both groups (all P values <0.05). CONCLUSIONS: We found sympathetic predominance during active state in fetuses around term. Post term parasympathetic modulation during rest was increased compared to near term.

Fetal movement quantification by fetal vectorcardiography: a preliminary study.

Fetal movement is a valuable source of information to monitor the neurological development of the fetus and assess fetal health. Currently, fetal movement can be assessed by the mother or detected by analysis of ultrasound images. Long-term monitoring of movement is complicated with both these methods as maternal self-assessment has a relatively poor sensitivity and specificity and automatic analysis of ultrasound images is not available. Moreover, ultrasound transducers transmit energy into the body, potentially endangering fetal health. In this paper, an alternative method for fetal movement monitoring is presented. This method operates by estimating and analyzing the fetal vectorcardiogram (VCG) from non-invasive recordings on the maternal abdomen. The determined fetal movement is compared with that assessed from a simultaneously performed ultrasound recording; the results of the presented method are consistent with the ultrasound images. In addition, the presented method enables quantification of the rotation angles by means of analysis of the rotation matrix between consecutive fetal VCGs, providing a tool for long-term monitoring of fetal movement with increased specificity.