Adaptive multiscale complexity analysis of fetal heart rate

Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space

Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originality of the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimal detection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (≃ 1250 subjects) collected at a French academic public hospital.

Sampling frequency of fetal heart rate impacts the ability to predict pH and BE at birth: a retrospective multi-cohort study

Fetal heart rate (FHR) sampling rate used on the bedside is equal or less than 4 Hz. Current FHR analysis methods fail to detect incipient fetal acidemia. In a fetal sheep model of human labour we showed that FHR sampling rates near 1000 Hz are needed to detect fetal acidemia. Trans-abdominal fetal ECG (t-a fECG) sampling FHR at 900 Hz combined with a complex signals bioinformatics approach showed promise in a human cohort. Here we validate this finding in a retrospective human cohort study by comparing the performance of the same bioinformatics approach to predict pH and BE at birth in the cohorts with FHR sampled either at 4 Hz or at 900 Hz.The 4 Hz FHR recording data sets consisted of the open access intrapartum CTG data base with n = 552 subjects used to develop the predictive model and another cohort of prospectively recruited n = 11 labouring women to then validate it. 900 Hz FHR data set comprised two prospectively recruited t-a fECG cohorts of n = 60 and n = 23 subjects. Recruitment criteria were similar across the cohorts. We have determined the goodness of fit (R(2)) and root mean square error (RMSE) as the performance indicators of the model on each cohort.The clinical characteristics of all cohorts were similar (gestational age 280   ±   8 d; gender 50% male; birth body weight 3.5   ±   0.5 kg; pH and BE at birth 7.25   ±   0.1 and  -5.7   ±   3.4 mmol L(  -  1), respectively; 1' and 5' Apgar scores at birth 8.5   ±   1.4 and 9.4   ±   0.6, respectively). The 4 Hz FHR cohort rendered-for pH and BE-R(2) = 0.26 and 0.2 and RMSE = 0.087 and 3.44, respectively. This could not be confirmed in the validation cohort for neither pH nor BE prediction. The 900 Hz FHR cohort rendered-for pH and BE-R(2) = 0.9 and 0.77 and RMSE = 0.03 and 1.70, respectively, and the pH prediction was validated.In our model, lower FHR sampling rate increased the predicted error range ~3-4 fold. We show that increasing FHR sampling rate to 900 Hz improves prediction of fetal pH and BE at birth. This should improve early identification of babies at risk of brain injury.

Scattering transform for intrapartum fetal heart rate variability fractal analysis: a case-control study

Intrapartum fetal heart rate monitoring, aiming at early acidosis detection, constitutes an important public health stake. Scattering transform is proposed here as a new tool to analyze intrapartum fetal heart rate (FHR) variability. It consists of a nonlinear extension of the underlying wavelet transform, that thus preserves its multiscale nature. Applied to an FHR signal database constructed in a French academic hospital, the scattering transform is shown to permit to efficiently measure scaling exponents characterizing the fractal properties of intrapartum FHR temporal dynamics, that relate not only to the sole covariance (correlation scaling exponent), but also to the full dependence structure of data (intermittency scaling exponent). Such exponents are found to satisfactorily discriminate temporal dynamics of healthy subjects (from that of nonhealthy ones) and to emphasize the role of the highest frequencies (around and above 1 Hz) in intrapartum FHR variability. This permits us to achieve satisfactory classification performance that improves on those obtained from the analysis of International Federation of Gynecology and Obstetrics (FIGO) criteria, notably by classifying as healthy a number of subjects that were incorrectly classified as nonhealthy by classical clinically used FIGO criteria. Combined to obstetrician annotations, these scaling exponents enable us to sketch a typology of these FIGO-false positive subjects. Also, they permit us to monitor the evolution along time of the intrapartum health status of the fetuses and to estimate an optimal detection time-frame.

Open access intrapartum CTG database

Background

Cardiotocography (CTG) is a monitoring of fetal heart rate and uterine contractions. Since 1960 it is routinely used by obstetricians to assess fetal well-being. Many attempts to introduce methods of automatic signal processing and evaluation have appeared during the last 20 years, however still no significant progress similar to that in the domain of adult heart rate variability, where open access databases are available (e.g. MIT-BIH), is visible. Based on a thorough review of the relevant publications, presented in this paper, the shortcomings of the current state are obvious. A lack of common ground for clinicians and technicians in the field hinders clinically usable progress. Our open access database of digital intrapartum cardiotocographic recordings aims to change that.

Description

The intrapartum CTG database consists in total of 552 intrapartum recordings, which were acquired between April 2010 and August 2012 at the obstetrics ward of the University Hospital in Brno, Czech Republic. All recordings were stored in electronic form in the OB TraceVue®;system. The recordings were selected from 9164 intrapartum recordings with clinical as well as technical considerations in mind. All recordings are at most 90 minutes long and start a maximum of 90 minutes before delivery. The time relation of CTG to delivery is known as well as the length of the second stage of labor which does not exceed 30 minutes. The majority of recordings (all but 46 cesarean sections) is – on purpose – from vaginal deliveries. All recordings have available biochemical markers as well as some more general clinical features. Full description of the database and reasoning behind selection of the parameters is presented in the paper.

Conclusion

A new open-access CTG database is introduced which should give the research community common ground for comparison of results on reasonably large database. We anticipate that after reading the paper, the reader will understand the context of the field from clinical and technical perspectives which will enable him/her to use the database and also understand its limitations.

Framework for adaptive multiscale analysis of nonhomogeneous point processes

We develop the methodology for hypothesis testing and model selection in nonhomogeneous Poisson processes, with an eye toward the application of modeling and variability detection in heart beat data. Modeling the process' non-constant rate function using templates of simple basis functions, we develop the generalized likelihood ratio statistic for a given template and a multiple testing scheme to model-select from a family of templates. A dynamic programming algorithm inspired by network flows is used to compute the maximum likelihood template in a multiscale manner. In a numerical example, the proposed procedure is nearly as powerful as the super-optimal procedures that know the true template size and true partition, respectively. Extensions to general history-dependent point processes is discussed.