Novel heart rate parameters for the assessment of autonomic nervous system function in premature infants.

Autonomic nervous system (ANS) balance is a key factor in homeostatic control of cardiac activity, breathing and certain reflex reactions such as coughing, sneezing and swallowing and thus plays a crucial role for survival. ANS impairment has been related to many neonatal pathologies, including sudden infant death syndrome (SIDS). Moreover, some conditions have been identified as risk factors for SIDS, such as prone sleep position. There is an urgent need for timely and non-invasive assessment of ANS function in at-risk infants. Systematic measurement of heart rate variability (HRV) offers an optimal approach to access indirectly both sympathetic and parasympathetic influences on ANS functioning. In this paper, data from premature infants collected in a sleep physiology laboratory in the NICU are presented: traditional and novel approaches to HRV analyses are applied and compared in order to evaluate their relative merits in the assessment of ANS activity and the influence of sleep position. Indices from time domain and nonlinear approaches contributed as markers of physiological development in premature infants. Moreover, significant differences were observed as a function of sleep position.

Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses.

The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying data mining techniques to FHR parameters, obtained from recordings in a population of 122 fetuses (61 healthy and 61 IUGRs), through standard CTG non-stress test. We computed N=12 indices (N=4 related to time domain FHR analysis, N=4 to frequency domain and N=4 to non-linear analysis) and normalized them with respect to the gestational week. We compared, through a 10-fold crossvalidation procedure, 15 data mining techniques in order to select the more reliable approach for identifying IUGR fetuses. The results of this comparison highlight that two techniques (Random Forest and Logistic Regression) show the best classification accuracy and that both outperform the best single parameter in terms of mean AUROC on the test sets.

IUGR management: new perspectives.

AIM OF THE STUDY: Analyzing velocimetric (umbilical artery, UA; ductus venosus, DV; middle cerebral artery, MCA) and computerized cardiotocographic (cCTG) (fetal heart rate, FHR; short term variability, STV; approximate entropy, ApEn) parameters in intrauterine growth restriction, IUGR, in order to detect early signs of fetal compromise. POPULATION STUDY: 375 pregnant women assisted from the 28th week of amenorrhea to delivery and monitored through cCTG and Doppler ultrasound investigation. The patients were divided into three groups according to the age of gestation at the time of delivery, before the 34th week, from 34th to 37th week, and after the 37th week. Data were analyzed in relation to the days before delivery and according to the physiology or pathology of velocimetry. Statistical analysis was performed through the t-test, chi-square test, and Pearson correlation test (P < 0.05). Our results evidenced an earlier alteration of UA, DV, and MCA. The analysis between cCTG and velocimetric parameters (the last distinguished into physiological and pathological values) suggests a possible relation between cCTG alterations and Doppler ones. The present study emphasizes the need for an antenatal testing in IUGR fetuses using multiple surveillance modalities to enhance prediction of neonatal outcome.

Multivariate analysis based on linear and non-linear FHR parameters for the identification of IUGR fetuses.

Fetal Heart Rate (FHR) monitoring represents a powerful tool for checking the arousal of pathological fetal conditions during pregnancy. This paper proposes a multivariate approach for the discrimination of Normal and Intra Uterine Growth Restricted (IUGR) fetuses based on a small set of parameters computed on the FHR signal. We collected FHR recordings in a population of 120 fetuses (60 normals and 60 IUGRs) at approximately the same gestational week through a standard CTG non-stress test. A set of 8 linear and non-linear indices were selected and computed on each recording, on the basis of their "stand-alone" discriminative properties, demonstrated in previous studies. By using the Orange® data mining suite we checked various multivariate discrimination models. The results show that a Logistic Regression performed on a limited set of only 4 parameters can reach 92.5% accuracy in the correct identification of fetuses, with 93% sensitivity and 91.5% specificity.

Extraction of fetal heart rate from maternal surface ECG with provisions for multiple pregnancies.

Twin pregnancies carry an inherently higher risk than singleton pregnancies due to the increased chances of uterine growth restriction. It is thus desirable to monitor the wellbeing of the fetuses during gestation to detect potentially harmful conditions. The detection of fetal heart rate from the maternal abdominal ECG represents one possible approach for noninvasive and continuous fetal monitoring. Here, we propose a new algorithm for the extraction of twin fetal heart rate signals from maternal abdominal ECG recordings. The algorithm detects the fetal QRS complexes and converts the QRS onset series into a binary signal that is then recursively scanned to separate the contributions from the two fetuses. The algorithm was tested on synthetic singleton and twin abdominal recordings. It achieved an average sensitivity and accuracy for QRS complex detection of 97.5% and 93.6%, respectively.

Predictive modeling of cardiovascular complications in incident hemodialysis patients.

The administration of hemodialysis (HD) treatment leads to the continuous collection of a vast quantity of medical data. Many variables related to the patient health status, to the treatment, and to dialyzer settings can be recorded and stored at each treatment session. In this study a dataset of 42 variables and 1526 patients extracted from the Fresenius Medical Care database EuCliD was used to develop and apply a random forest predictive model for the prediction of cardiovascular events in the first year of HD treatment. A ridge-lasso logistic regression algorithm was then applied to the subset of variables mostly involved in the prediction model to get insights in the mechanisms underlying the incidence of cardiovascular complications in this high risk population of patients.

Body composition and heart rate variability to achieve dry weight and tolerance.

Autonomic dysfunction in patients with end- stage renal disease is associated with poor prognosis. Heart rate variability (HRV), determined by the standard deviation of the normal R- R interval, has been reported to be a useful evaluation of cardiac autonomic modulation. The relationship between HRV and hydration status (HS) can be analyzed by whole body bioimpedance spectroscopy. This allows a classification of patients according the combination of HS with predialysis systolic blood pressure. Differences in HRV can be studied in patients with high over hydration, but normal or low blood pressure, with respect to fluid-overloaded/hypertensive patients and normohydrated/normotensive patients. In conclusion, the assessment of the autonomic nervous system response to the hemodialysis treatment in end- stage renal disease patients, classified according to a reliable and quantitative measurement of their fluid overload, could permit better management of both arterial blood pressure and HS.

Advances in monitoring cardiovascular signals. Contribution of nonlinear signal processing.

Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their status, thus providing necessary interventions in time. To obtain this important objective it is necessary to integrate technological development with systems performing biomedical knowledge extraction and classification. Methods extracting non linear characteristics from HRV signal are presented and discussed to stress that integrated and multiparametric signal processing approaches can contribute to new diagnostic and classification indices. Examples report heart rate variability analysis in long periods in patients with cardiovascular disease. Fetal ECG monitoring is another example. In this case, coupling nonlinear parameters and linear time and frequency techniques increases diagnostic power and reliability of the monitoring. The paper shows that integrated signal analysis is very helpful to describe pathophysiological mechanisms involved in the cardiovascular and neural system control. It is a reliable basis to set up knowledge-based monitoring systems.

Telefetalcare: a first prototype of a wearable fetal electrocardiograph.

Fetal heart rate monitoring is fundamental to infer information about fetal health state during pregnancy. The cardiotocography (CTG) is the most common antepartum monitoring technique. Abdominal ECG recording represents the most valuable alternative to cardiotocography, as it allows passive, non invasive and long term fetal monitoring. Unluckily fetal ECG has low SNR and needs to be extracted from abdominal recordings using ad hoc algorithms. This work describes a prototype of a wearable fetal ECG electrocardiograph. The system has flat band frequency response between 1-60 Hz and guarantees good signal quality. It was tested on pregnant women between the 30(th) and 34(th) gestational week. Several electrodes configurations were tested, in order to identify the best solution. Implementation of a simple algorithm for FECG extraction permitted the reliable detection of maternal and fetal QRS complexes. The system will allow continuative and deep screening of fetal heart rate, introducing the possibility of home fetal monitoring.

Temporal and spatial analysis of astrocyte calcium waves.

In the last decade new ideas were born about the temporal and spatial dynamics of intercellular calcium waves in astrocytes. In this paper we introduce a new approach to analyze the ways in which astrocytes communicate in cultures. We present a method to describe the spatial propagation of Ca(2+) waves in vitro and a technique to compare the activity of different cells in vivo and in vitro under different stimulation conditions. The proposed method resulted to be an interesting way to distinguish different astrocyte clusters, which can be related to the communication characteristics in the network.

Modeling spiking activity of in vitro neuronal networks through non linear methods.

Neuroscience research is even more exploiting technologies developed for electronic engineering use: this is the case of Micro-Electrode Array (MEA) technology, an instrumentation which is able to acquire in vitro neuron spiking activity from a finite number of channels. In this work we present three models of synaptic neuronal network connections, called 'Full-Connected', 'Hierarchical' and 'Closed-Path'. Related to each one we implemented an index giving quantitative measures of similarity and of statistical dependence among neuron activities recorded in different MEA channels. They are based on Information Theory techniques as Mutual and Multi Information: the last one extending the pair-wise information to higher-order connections on the entire MEA neuronal network. We calculated indexes for each model in order to test the presence of self-synchronization among neurons evolving in time, in response to external stimuli such as the application of chemical neuron-inhibitors. The availability of such different models helps us to investigate also how much the synaptic connections are spatially sparse or hierarchically structured and finally how much of the information exchanged on the neuronal network is regulated by higher-order correlations.

Non linear methods estimating neural network behavior on Micro-Electrode Array technology.

In the recent years Neuroscience research is exploiting technologies initially developed only for electronic engineering use: this is the case of Micro-Electrode Array (MEA) technology, where a finite number of channels acquires in vitro neural spiking activity. In this work we present a new method to process time data series from MEA trough an ad-hoc software-framework. Our aim is to build a classifier giving quantitative measures of similarity and statistical dependence among neurons activities recorded in different MEA channels. Methods applied to extract specific information about neuronal behavior are Mutual Information and Dynamic Time Warping. In order to extend the pair-wise information so obtained to the entire neuronal networks on MEA, we have chosen to implement a sub-optimal criterion thanks to Genetic Algorithms (GA): this technique support us to sort MEA channels based on dependent activity, thus providing a global index. We applied it to test the presence of self-synchronization among neurons, which can evolve in time and adapt their self in response to specific external stimuli, such as those of the chemical neuron-inhibitors here analyzed.

Comparison between fetal heart rate standard parameters and complexity indexes for the identification of severe intrauterine growth restriction.

OBJECTIVES: The intrauterine growth restriction (IUGR) is a pathological state: the fetus is at risk of hypoxia and this condition is associated with increased perinatal morbidity and mortality. However, evidence-based guidelines for clinical surveillance are poor and lack reliable indexes. This study introduces new procedures to extract parameters from the fetal heart rate signal in order to identify severe intrauterine growth restricted (IUGR) fetuses METHODS: Standard parameters (time domain and frequency domain indexes) are compared to a new parameter, the Lempel Ziv complexity, and to two regularity estimators (approximate entropy and sample entropy). The paper analyzes the robustness of the indexes coming from the parameter extraction procedure. RESULTS AND CONCLUSIONS: The results show that the LZ complexity is a stable parameter and it is able to significantly discriminate the severe IUGR (preterm delivered) from moderate IUGR (at term delivered) and from healthy fetuses.

Nonlinear analysis of heart rate variability signal for the characterization of cardiac heart failure patients.

The purpose of this work is to characterize the heart rate variability (HRV) of patients affected by congestive heart failure (CHF) and to find out the main difference between this pathological condition and the physiological state. Parameters adopted in this work are: the detrended fluctuation analysis (DFA) and the Higuchi exponent to assess long correlations and self-similarity; the regularity estimators, approximate entropy (ApEn) and sample entropy (SampEn) and the multiscale entropy (MSE). Furthermore we proposed a new regularity index, the Gaussian entropy (GaussEn) which is a modification of the previous ApEn and SampEn. The results show the proposed parameters do an effective separation of physiological and pathological subject conditions. These results are part of a study evaluating the nonlinear index prognostic value toward cardiac death.

A system for prevention, care and rehabilitation of subject with cardiovascular risk: the signal processing algorithm library.

In the last years the home monitoring development is increased both for its capability as a real time tool to manage patients health and to reduce hospitalization costs. The home monitoring system is a complex structure that needs the collaboration of different disciplines, from medicine to engineering, and technologies. This project has been developed with the integration of different groups of research as to unify all the necessary knowledge. According to physician exigencies a signal processing library has been implemented to describe in a synthetic and effective way the pathological status of patients with moderate cardiovascular risk. Consequently a software and hardware architecture have been designed to acquire ECG signal, to extract HRV and respiratory information through a multiparametric approach and to store the results. This home monitoring system has been projected to work during an appropriate physical training section and its function is both diagnostic and therapeutic as well as for rehabilitation. The aim of this work is to describe the structure of the signal processing library.

Self-similarity behavior characterization of fetal heart rate signal in healthy and intrauterine growth retarded fetuses.

In this paper we deal with the problem of the interpretation of the fetal heart rate (FHR) signal. From literature is known that FHR contains both linear and non linear components. Starting from this consideration we analyzed FHR as a fractal time series and we evaluated its self similarity behavior using the Hurst's coefficient (H). We first evaluated the stationarity of FHR time series and then we estimated H with Detrend fluctuation analysis (DFA) method. We calculated Hurst's coefficient for healthy fetuses and for fetuses affected by Intrauterine grow retardation (IUGR). Results provided H = 0.350 +/- 0.064 (avg +/- std) for healthy patients and H = 0.461 +/- 0.059 for IUGR. It is also shown that IUGR patients exhibit a "less non-stationary" and longer-memory behavior than normals with a reduced information content of FHR signal. We propose for this phenomenon a physiological explanation connected with the abnormal autonomic nervous system development of IUGR patients.

Fetal heart rate variability due to vibroacoustic stimulation: linear and nonlinear contribution.

OBJECTIVES: This work aims at characterizing the variation of fetal heart rate (FHR) provoked by vibroacoustic 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.

Complexity analysis of 24 hours heart rate variability time series.

We propose to study the heart rate variability (HRV) time series complexity by computing the Lempel Ziv complexity measure. LZ is sensitive to the rate of pattern recurrences in a time series. Analysis considers 24 h HRV time series of healthy subjects and patients with cardiovascular diseases. Analysis with simulated signals show the LZ measure can vary depending on the adopted coding process. The binary coding, proposed in this work, is sensitive to the different dynamical systems generating the time series, as the ternary coding is sensitive to the presence of stationary states, i.e. a consecutive repetition of the same RR interval value. LZ method reliably differentiates healthy vs. disease group. Further clinical investigations on the LZ complexity and on its relationship to the risk of sudden death, can supply new diagnostic indications.

Nonlinear analysis of heart rate variability signal: physiological knowledge and diagnostic indications.

The complex structure of the heart rate variability signal (HRV) has been widely studied in order to identify the "complex" nature of its control mechanisms. By adopting methods based on the reconstruction of the HRV time series, in an embedding space, the fractal dimension and the Lyapunov exponents can be computed. These estimations must be associated to a determinism test based on surrogate data, confirming that it is a deterministic instead of a linear correlation mechanism that controls the HRV dynamics. Results in 24 hours HRV series confirm that the structure generating the signal is neither linear nor stochastic. Furthermore, methods quantifying fractal and self-similar "monofractal" characteristics (1/f/sup alpha/ spectrum, detrended fluctuation analysis, DFA) and a regularity statistic (approximate entropy, ApEn), allow characterizing the HRV signal and distinguishing pathological from healthy subjects. Results in the HRV signal analysis confirm the presence of a nonlinear deterministic structure in time series. Moreover, nonlinear parameters can be used to separate normal from pathological subjects. Application examples are shown concerning cardiovascular pathologies and fetal heart rate analysis.