Summary Objectives: This work aims at characterizing the variation of fetal heart rate (FHR) provoked by vibro-acoustic stimulation (VAS). The FHR signal is analyzed by means of a multiparametric approach consisting of linear and nonlinear indices. Methods: The FHR signals of 13 fetuses were collected through a US standard CTG monitor (HP1351A) and were sampled at a frequency of 2 Hz. The VAS was provided after a period of quiet of 10 minutes. The analysis was performed on the quiet period and on two successive time windows of 10 minutes each, after the stimulation. FHR classical parameters (delta, short term variability, long term irregularity, accelerations and decelerations) as well as power spectral density (PSD) and approximate entropy (ApEn) were computed for each period. Results: Results confirm that there is a significant change in fetal conditions after the stimulus is applied. This change can be clearly observed either in time domain parameters and in the regularity index (ApEn). Individual data are all consistent with an increase of variability and a decrease of regularity after VAS. Conclusions: The obtained results give further strength to the hypothesis that vibratory stimuli tests represent a reliable method for monitoring the neural development of the fetus during pregnancy.

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies.

Vibroacoustic stimulation is able to produce a fetal sensory response, which is mediated by the Central Nervous System (CNS). Despite the fetal heart rate variability is basically regulated by Autonomic Nervous System (ANS), some changes in the FHR appear as a direct consequence of the sensory stimulation. The paper proposes an analysis of FHR of 13 normal subjects based on nonlinear approaches. Detrended Fluctuation Analysis (DFA), Approximate Entropy and Poincare plots are able to evidence differences that can be attributed to fetal nervous system activity. In particular DFA decreases after stimulation confirming a reduction in the longterm correlation properties of FHR. The parallel, marked increase of ApEn indicates a regularity loss after the sensory stimuli. Nonlinear indexes seem able to describe complex interactions of different neural mechanisms which control the FHR.

Heart Rate Variability analysis has demonstrated as a powerful diagnostic toot in many disease conditions which involve an alteration of the physiological control systems. In this paper we propose to classify Fetal Heart Rate signals through a set of indexes including time domain, frequency domain and other parameters related to signal morphology and regularity. This set is used as the input of an automatic system, whose goal is to detect the risk for the fetus to enter a pathological state. On a database of more than 400 recordings, we tested different classification methods to identify normals from potential pathological fetuses. A neural network approach was compared with classical statistical methods. The multilayer pereeptron, trained with the adaptive backpropagation algorithm, performed better than any tested statistical classifier.

Proposes new classifiers based on fuzzy inference systems (FISs) for the Fetal Heart Rate (FHR) signal analysis. They include standard cardiotocographic (CTG) parameters together with a set of frequency domain and nonlinear indices. The goal is the identification of two very common fetal pathological conditions: Intra-Uterine Growth Retardation (IUGR) and Diabetes type I. The FHR signals obtained from 104 CTG recordings were analyzed (75 Normal, 11 IUGR and 18 Diabetic). Fuzzy classifiers combine the set. Of 10 input data into the S-output set (Normal, IUGR, Maternal Diabetes) by fuzzy relies. Results show FISs predict normal and pathological fetal states even with 100% of correct classifications. Their performance however is always higher than 80% in the whole population, depending on the rule number. This approach can strongly help the automatic CTG signal analysis improving the early discrimination among normal and pathological fetal conditions

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and low-price tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless very poor indications on fetal pathologies can be inferred from the actual CTG analysis methods, either they consist of the clinician eye inspection or of automatic algorithms. A relevant amount of this unsatisfactory performance resides on the weakness of methods used for classifying fetal conditions and generate a risk alarm during pregnancy. In the paper three neural classifiers are proposed to discriminate among fetal behavioral states and among normal and pathological fetal conditions, on the basis of CTG recordings. All classifiers are fed by indexes extracted from fetal heart rate signal. Results show very promising performance towards the prediction of fetal outcomes on the set of collected FHR signals.

One neural and one fuzzy classifier are proposed to discriminate among normal and pathological fetal conditions during pregnancy. Both classifiers are based on linear and nonlinear indexes extracted from cardiotocographic fetal monitoring. Results low very promising performance on the set of collected fetal heart rate signals

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and low-price tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless very poor indications on fetal pathologies can be inferred from the actual CTG analysis methods, either they consist of the clinician eye inspection or of automatic algorithms. It is certain that fetal heart rate and uterine contraction carry much more information on fetal state than it is extracted by classical analysis methods. In particular Fetal Heart Rate (FHR) signal has demonstrated to provide consistent indication of his well being status and in case of fetal stress, during labor, the FHR usually shows some morphological alterations. As the methods actually used for judging a CTG trace as "abnormal" give a too low predictive value for fetal dangers, we started to develop a new computerized system for the CTG analysis. The fetal monitoring system is based on a new multiparametric analysis of FHR which includes non-linear analysis algorithms (Approximate Entropy and space state maps) of FHR. The analysis is coupled with a classification of fetal states (ABCD) by means of Neural Networks. A comparison between supervised and unsupervised networks has been done on the same set of recordings. A prototype of this new monitoring system will be implemented on the basis of HP Traceview distributed architecture. INTRODUCTION The introduction of cardiotocography (CTG) as a non-invasive technique for monitoring fetal conditions, allowed obstetricians to direct their attention to the antepartum period, on the basis that a major portion of the unfavorable fetal outcomes seems due to events that occur prior to the onset of labour (van Gejin, 1996). As a matter of fact, a number of risky conditions for fetal compromise has been recognized in the antepartum period, of which intrauterine growth retardation (IUGR) due to uteroplacental insufficiency and maternal type I diabetes are the predominant. Since its introduction in the clinical routine, the use of CTG for antepartum fetal monitoring has led to a drastic reduction of intrapartum and precocious child mortality. However the actual methodology employed for judging CTG tracings has demonstrated a low predictive value for the fetal danger and high value of false positives in most cases. Indeed in the last 25 years since the introduction of CTG analysis, although the fetal and neonatal death rates have fallen considerably, the risk and severity of neurological handicap may even be rising. The conclusion of a number of recent studies is that very poor indications about fetus/newborn illness could be inferred from the actual CTG analysis (van Gejin, 1996), (Dawes et al. 1996). The most reliable indicator of fetal condition is represented by fetal heart rate (FHR) signal, upon which CTG is based. In case of fetal stress there is a high probability (>90%) that FHR, during labour, will show some anomalies or alterations, while if FHR recording seems to be normal, chances are high that the fetus can stand the labour. Several conditions such as hypoxia, acidemia, drug induction produce noticeable variations of FHR, which are usually detected by simple eye inspection of the physician. In essence, the main characteristic of the FHR is thought to be the presence of a baseline (sinusal rhythm) on which the frequency control mechanisms act by provoking some irregularities called accelerations and decelerations. (Mantel 1990). Up to now CTG records have been analyzed by detecting and classifying mainly the changes of that hypothetical FHR base value (accelerations and decelerations) in the hope of revealing a fetal sufferance status. Some CTG systems (Sonicaid, Hp) have tried an automatic classification of fetal states, based on the attempt to reproduce the criteria used by the clinicians, although in a quantitative way. In addition to the identification of accelerations and decelerations, also a quantitative assessment of the short term (STV) and long term (LTV) variability has been performed by the computerized CTG diagnostic systems. However the algorithmic approach, implemented on computerized automatic CTG diagnostic systems, has only led to a reduction of inter and intraobserver variability. An EEC project (Perinatal Monitoring) was pointed out to test the only commercially available system (Sonicaid System 8000) with an initial multitrial research (van Geijn, 1993) and the results did not show a significant clinical improvement from the classic analysis by eye inspection to the automatic one. On the other hand, recent studies on HR variability signal of adult and newborn subjects emphasize that both linear and nonlinear effects contribute to the signal generation pattern (Signorini et al., 1992 and 1994). In addition it was noted that fetal distress was preceded by alterations in interbeat intervals before any appreciable change occurred in heart rate itself. If observed on long periods of time, the series obtained from the HR values are highly irregular, typical of nonlinear system behavior. Moreover, using even nonlinear analysis techniques can cluster pathological conditions. All these results lead to think that FHR regulation mechanisms show an intrinsic nonlinear behavior, i.e. FHR values can highly oscillate in time and not always tend to an equilibrium state or to a sinusal rhythm. Thus, FHR variation contains the information about the neural events controlling fetal heart, although the methodological tools used for clinical diagnosis up to now did not allow to extract reliable quantitative indexes linking physiopathological fetal states with FHR signal patterns. For that reason we decided to face the problem of extracting from FHR signal both a classification of FHR patterns through ANNs, which are known to behave as non-linear classifiers, and new indexes of the non-linear behavior of FHR, presenting high sensitivity with respect to normal and pathological fetal states. DEVELOPMENT OF A NEW FETAL MONITORING SYSTEM The project we are developing has the goal to realize a new system for monitoring fetal condition, based on an appropriate and reliable analysis of FHRV. The final product of this work will be a clinical instrumentation prototype whose main characteristics are illustrated in the following. The whole system will be implemented on a PC workstation on a Windows platform. Its medium/low cost is crucial for being used in most obstetrical units. We identify two main features that will be the kernel of a new instrument for the fetal cardiotocographic analysis. Most of these are yet implemented and have been tested on an annotated set of CTG signals as will be illustrated in the Results section. 1. System learning ability This feature is obtained by means of Neural Network (NN) techniques for the diagnostic classification of FHR patterns based on the new set of relevant parameters identified by the new mathematical tools illustrated in the following. NNs have the peculiarity of being able to learn how to perform nonlinear classification of the input vectors once trained with an appropriate set of examples. Depending on the network architecture the training can be supervised – i.e. the training set consist of couples of input and desired output vectors or unsupervised – the learning process needs only the input vectors. 2. Linear and nonlin ear multiparametric analysis of FHR We evaluate the FHR characteristics by calculating linear parameters (Power spectral density estimation, variance), by extracting regularity parameters (Approximate Entropy) and by representing signal variations through Delay Maps. The new System Build-up has been carried out trough a modification of both the hardware and the software of an existing computerized CTG system (HP-2CTG) based on the HP-M1351A ultrasound fetal monitor. We started from the HP-2CTG system and we modified the FHRV sampling frequency, passing from the collection of 1 FHR value every 2.5 sec to 1 FHR value every 0.5 or 0.25 sec. Following the modification of the FHR sampling frequency, the software was adapted for extracting, in a reliable way, the standard parameters (such as FHR accelerations, deceleration, etc.). Then, a first step for providing the system with learning ability was the implementation of two different NN architectures for the classification of FHR patterns based on input vectors consisting of 15 parameters automatically extracted by the 2CTG system. Once the NN has reached a stable state, the obtained clusters will be analyzed on the basis of the actual pathophysiological knowledge of FHRV indexes. The NN will then be tuned with the cases collected in the clinical tests in order to identify precise pathological classes. The final setup consists of a supervised NN, which will be implemented on the prototype system and trained with the clinical cases collected during the project. This NN can be re-trained by the end-user with his own cases. The second step consisted of the extraction of linear and nonlinear FHR global indexes including variance and Approximate Entropy. ApEn parameter quantifies the amount of regularity in data performing a detection of differences in HR that are not singled out by other classical analysis. Higher ApEn values indicate greater randomness in HR pulse. In this way, the normal fetal development should be characterized by an increasing of irregularity in HRV. As a matter of fact the complexity and the regularity properties of FHR dynamics can be useful to classify pathological situation as obtained for adult subjects and newborn infants. On the fetal data obtained from this system we compute of the power spectral density (PSD) of the FHR by means computation based on the autoregressive modeling approach. PSD analysis provides tools for a better identification of heart rate patterns related t