Die fetale Herzfrequenzvariation in der Magnetokardiografie und der Kardiotokografie - direkter Vergleich beider Verfahren

Can fetal heart rate variability obtained from cardiotocography provide the same diagnostic value like from electrophysiological interbeat intervals?

OBJECTIVE

Fetal heart rate variability (HRV) is widely used for monitoring fetal developmental disturbances. Only expensive fetal magnetocardiography (fMCG) allows the precise recording of the individual fetal heart beat intervals uncovering also highly frequent vagal modulation. In contrast, transabdominal fetal electrocardiography (fECG) suffers from noise overlaying the fetal cardiac signal. Cardiotocography (CTG) is the clinical method of choice, however, based on Doppler ultrasound, improper to resolve single beats concisely. The present work addresses the transferability of established electrophysiological HRV indices to CTG recordings during the fetal maturation period of 20-40 weeks of gestation (WGA).

APPROACH

We compared (a) HRV indices obtained from fMCG, CTG and fECG of short-term amplitude fluctuations (sAMPs) and long-term amplitude fluctuations (lAMPs) and complexity, and (b) their diagnostic value for identifying maturational age, fetal growth restriction (FGR) and small for gestational age (SGA). We used the functional brain age score (fABAS) and categories of long- and short-term regulation and complexity.

MAIN RESULTS

Integrating all substudies, we found: (a) indices related to long-term regulation, and with modified meaning and values of short-term regulation and sympathovagal balance (SVB) according to electrophysiological HRV standards can be obtained from CTG. (b) Models using HRV indices calculated from CTG allow the identification of maturational age and discriminate FGR from controls with almost similar precision as electrophysiological means. (c) A modified set of HRV parameters containing short- and long-term regulation and long-term/short-term ratio appeared to be most suitable to describe autonomic developmental state when CTG data is used.

SIGNIFICANCE

Whereas the predominantly vagally modulated beat-to-beat precise high frequencies of HRV are not assessable from CTG, we identified relevant related HRV indices and categories for CTG recordings with diagnostic potential. They require further evaluation and confirmation with respect to any issues of fetal developmental and perinatal problems in subsequent studies. This methodology significantly extends the measures of established CTG devices. Novelty and significance HRV indices provide predestinated diagnostic markers of autonomic control in fetuses. However, the established CTG does not provide the temporal precision of electrophysiological recordings. Beat-to-beat related, mainly vagally modulated behavior is not exactly represented in CTG. However, a set of CTG-specific HRV indices that are mainly comparable to established electrophysiological HRV parameters obtained by magnetocardiography or electrocardiography provided almost similar predictive value for fetal maturational age and were helpful in characterizing FGR. These results require validation in the monitoring of further fetal developmental disturbances. We recommend a corresponding extension of CTG methodology.

Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms

BACKGROUND

Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths.

METHODS

This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH < 7.25-foetal deterioration, n = 4; or clinical decision without evidence of pathological outcome measures, n = 24). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures.

RESULTS

The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%.

CONCLUSIONS

The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies.

Fetal presentation of long QT syndrome--evaluation of prenatal risk factors: a systematic review

OBJECTIVE

This systematic review evaluated the existence of risk factors for the fetal manifestation of long QT syndrome (LQTS).

METHODS

Prenatal cardiac findings suggestive of fetal LQTS were studied using 30 English literature reports extracted from the Pubmed database (1979 to December 2011) using the search terms 'long QT syndrome', 'fetal arrhythmia' and 'congenital heart disease'.

RESULTS

LQTS accounted for 15-17% of fetal bradycardias <110 bpm among fetuses with a normally structured heart. Of the patients with significant prenatal findings of LQTS, 17-35% exhibited a reduced baseline fetal heart rate (FHR) of 110-120 bpm on electronic cardiotocography. Other prenatal signs were sinus or intermittent bradycardia <110 bpm arising from atrioventricular block, tachyarrhythmias, pleural effusion and hydrops. More than 30% of Japanese infants with LQTS born at or after the mid-1980s exhibited the above-mentioned in utero signs.

CONCLUSIONS

Fetal factors including a slightly reduced baseline FHR of 110-120 bpm, bradycardia <110 bpm, tachyarrhythmias or clinical signs of heart failure, such as pleural effusion and hydrops, were associated with a higher frequency of LQTS. The use of these signs may help to increase the perinatal diagnosis of LQTS.

Heart rate features in fetal behavioural states

BACKGROUND AND AIM

Fetal behavioural states have been defined on the basis of eye movements, body movements and heart rate patterns as presented by cardiotocography (CTG). The aim of this work was to determine whether behavioural states can be distinguished on the basis of heart rate features alone using high resolution beat-to-beat fetal magnetocardiography.

STUDY DESIGN

Five minute magnetocardiograms were recorded at a sampling rate of 1 kHz in 40 healthy fetuses (36th-41st week of gestation). In the reconstructed RR interval time series, 256-beat epochs corresponding to the behavioural states 1F, 2F and 4F were visually identified according to heart rate patterns as defined for CTG. These epochs were then quantified using mean RR interval, its standard deviation (SDNN), its root mean square of successive difference (RMSSD) and on the basis of symbolic dynamics of short 8 beat trains.

RESULTS

Pairwise comparison between the behavioural states showed that the values of each of these measures differed significantly between the states. Quadratic discriminant analysis further revealed that mean RR interval and SDNN sufficed to classify state with a correct classification of 92%.

CONCLUSIONS

The results suggest that measures that quantify overall aspects of heart rate can distinguish RR interval time series which were classified into different fetal behavioural states. The differences in short-term variability as quantified by RMSSD and symbolic dynamics may help reveal new aspects of these states.