Frequency and Time Domain Analysis of Foetal Heart Rate Variability with Traditional Indexes: A Critical Survey

Analysis of fetal heart rate fluctuations in women diagnosed with preeclampsia during the latent phase of labor

Introduction

Fetal heart rate variability (fHRV) is a tool used to investigate the functioning of the fetal autonomic nervous system. Despite the significance of preeclampsia, fHRV during the latent phase of labor has not been extensively studied. This study aimed to evaluate fetal cardiac autonomic activity by using linear and nonlinear indices of fHRV analysis in women diagnosed with preeclampsia without hypertensive treatment during gestation, compared to normotensive women during the latent phase of labor.

Methods

A cross-sectional and exploratory study was conducted among pregnant women in the latent phase of labor, forming three study groups: normotensive or control (C, 38.8 ± 1.3 weeks of pregnancy, n = 22), preeclampsia with moderate features (P, 37.6 ± 1.4 weeks of pregnancy n = 10), and preeclampsia with severe features (SP, 36.9 ± 1.2 weeks of pregnancy, n = 12). None of the participants received anti-hypertensive treatment during their pregnancy. Linear and nonlinear features of beat-to-beat fHRV, including temporal, frequency, symbolic dynamics, and entropy measures, were analyzed to compare normotensive and preeclamptic groups.

Results

Significantly lower values of multiscale entropy (MSE) and short-term complexity index (Ci) were observed in the preeclamptic groups compared to the C group (p < 0.05). Additionally, higher values of SDNN (standard deviation of R-R intervals) and higher values of low-frequency power (LF) were found in the P group compared to the C group.

Conclusion

Our findings indicate that changes in the complexity of fetal heart rate fluctuations may indicate possible disruptions in the autonomic nervous system of fetuses in groups affected by undiagnosed preeclampsia during pregnancy. Reduced complexity and shifts in fetal autonomic cardiac activity could be associated with preeclampsia's pathophysiological mechanisms during the latent phase of labor.

Computer Vision for Identification of Increased Fetal Heart Variability in Cardiotocogram

INTRODUCTION

Increased fetal heart rate variability (IFHRV), defined as fetal heart rate (FHR) baseline amplitude changes of >25 beats per minute with a duration of ≥1 min, is an early sign of intrapartum fetal hypoxia. This study evaluated the level of agreement of machine learning (ML) algorithms-based recognition of IFHRV patterns with expert analysis.

METHODS

Cardiotocographic recordings and cardiotocograms from 4,988 singleton term childbirths were evaluated independently by two expert obstetricians blinded to the outcomes. Continuous FHR monitoring with computer vision analysis was compared with visual analysis by the expert obstetricians. FHR signals were graphically processed and measured by the computer vision model labeled SALKA.

RESULTS

In visual analysis, IFHRV pattern occurred in 582 cardiotocograms (11.7%). Compared with visual analysis, SALKA recognized IFHRV patterns with an average Cohen's kappa coefficient of 0.981 (95% CI: 0.972-0.993). The sensitivity of SALKA was 0.981, the positive predictive rate was 0.822 (95% CI: 0.774-0.903), and the false-negative rate was 0.01 (95% CI: 0.00-0.02). The agreement between visual analysis and SALKA in identification of IFHRV was almost perfect (0.993) in cases (N = 146) with neonatal acidemia (i.e., umbilical artery pH <7.10).

CONCLUSIONS

Computer vision analysis by SALKA is a novel ML technique that, with high sensitivity and specificity, identifies IFHRV features in intrapartum cardiotocograms. SALKA recognizes potential early signs of fetal distress close to those of expert obstetricians, particularly in cases of neonatal acidemia.

Exploring the Influence of Fetal Sex on Heart Rate Dynamics Using Fetal Magnetocardiographic Recordings

Fetal sex has been associated with different development trajectories that cause structural and functional differences between the sexes throughout gestation. Fetal magnetocardiography (fMCG) recordings from 123 participants (64 females and 59 males; one recording/participant) from a database consisting of low-risk pregnant women were analyzed to explore and compare fetal development trajectories of both sexes. The gestational age of the recordings ranged from 28 to 38 weeks. Linear metrics in both the time and frequency domains were applied to study fetal heart rate variability (fHRV) measures that reveal the dynamics of short- and long-term variability. Rates of linear change with GA in these metrics were analyzed using general linear model regressions with assessments for significantly different variances and GA regression slopes between the sexes. The fetal sexes were well balanced for GA and sleep state. None of the fHRV measures analyzed exhibited significant variance heterogeneity between the sexes, and none of them exhibited a significant sex-by-GA interaction. The absence of a statistically significant sex-by-GA interaction on all parameters resulted in none of the regression slope estimates being significantly different between the sexes. With high-precision fMCG recordings, we were able to explore the variation in fHRV parameters as it relates to fetal sex. The fMCG-based fHRV parameters did not show any significant difference in rates of change with gestational age between sexes. This study provides a framework for understanding normal development of the fetal autonomic nervous system, especially in the context of fetal sex.

Fetal heart rate spectral analysis in raw signals and PRSA-derived curve: normal and pathological fetuses discrimination

Cardiotocography (CTG) is the most common technique for electronic fetal monitoring and consists of the simultaneous recording of fetal heart rate (FHR) and uterine contractions. In analogy with the adult case, spectral analysis of the FHR signal can be used to assess the functionality of the autonomic nervous system. To do so, several methods can be employed, each of which has its strengths and limitations. This paper aims at performing a methodological investigation on FHR spectral analysis adopting 4 different spectrum estimators and a novel PRSA-based spectral method. The performances have been evaluated in terms of the ability of the various methods to detect changes in the FHR in two common pregnancy complications: intrauterine growth restriction (IUGR) and gestational diabetes. A balanced dataset containing 2178 recordings distributed between the 32nd and 38th week of gestation was used. The results show that the spectral method derived from the PRSA better differentiates high-risk pregnancies vs. controls compared to the others. Specifically, it more robustly detects an increase in power percentage within the movement frequency band and a decrease in high frequency between pregnancies at high risk in comparison to those at low risk.

Comprehensive viewpoints on heart rate variability at high altitude

OBJECTIVES

Hypoxia is a physiological state characterized by reduced oxygen levels in organs and tissues. It is a common clinicopathological process and a major cause of health problems in highland areas.  Heart rate variability (HRV) is a measure of the balance in autonomic innervation to the heart. It provides valuable information on the regulation of the cardiovascular system by neurohumoral factors, and changes in HRV reflect the complex interactions between multiple systems. In this review, we provide a comprehensive overview of the relationship between high-altitude hypoxia and HRV. We summarize the different mechanisms of diseases caused by hypoxia and explore the changes in HRV across various systems. Additionally, we discuss relevant pharmaceutical interventions. Overall, this review aims to provide research ideas and assistance for in-depth studies on HRV. By understanding the intricate relationship between high-altitude hypoxia and HRV, we can gain insights into the underlying mechanisms and potential therapeutic approaches to mitigate the effects of hypoxia on cardiovascular and other systems.

METHODS

The relevant literature was collected systematically from scientific database, including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Baidu Scholar, as well as other literature sources, such as classic books of hypoxia.

RESULTS

There is a close relationship between heart rate variability and high-altitude hypoxia. Heart rate variability is an indicator that evaluates the impact of hypoxia on the cardiovascular system and other related systems. By improving the observation of HRV, we can estimate the progress of cardiovascular diseases and predict the impact on other systems related to cardiovascular health. At the same time, changes in heart rate variability can be used to observe the efficacy of preventive drugs for altitude related diseases.

CONCLUSIONS

HRV can be used to assess autonomic nervous function under various systemic conditions, and can be used to predict and monitor diseases caused by hypoxia at high altitude. Investigating the correlation between high altitude hypoxia and heart rate variability can help make HRV more rapid, accurate, and effective for the diagnosis of plateau-related diseases.

The effect of maternal pregestational diabetes on fetal autonomic nervous system

Heart rate variability assessment of neonates of pregestational diabetic mothers have shown alterations in the autonomic nervous system (ANS). The objective was to study the effect of maternal pregestational diabetes on ANS at the fetal stage by combining cardiac and movement parameters using a non-invasive fetal magnetocardiography (fMCG) technique. This is an observational study with 40 participants where fetuses from a group of 9 Type 1, 19 Type 2 diabetic, and 12 non-diabetic pregnant women were included. Time and frequency domain fetal heart rate variability (fHRV) and coupling of movement and heart rate acceleration parameters related to fetal ANS were analyzed. Group differences were investigated using analysis of covariance to adjust for gestational age (GA). When compared to non-diabetics, the Type 1 diabetics had a 65% increase in average ratio of very low-frequency (VLF) to low-frequency (LF) bands and 63% average decrease in coupling index after adjusting for GA. Comparing Type 2 diabetics to non-diabetics, there was an average decrease in the VLF (50%) and LF bands (63%). Diabetics with poor glycemic control had a higher average VLF/LF (49%) than diabetics with good glycemic control. No significant changes at p < 0.05 were observed in high-frequency (HF) frequency domain parameters or their ratios, or in the time domain. Fetuses of pregestational diabetic mothers exhibited some differences in fHRV frequency domain and heart rate-movement coupling when compared to non-diabetics but the effect of fHRV related to fetal ANS and sympathovagal balance were not as conclusive as observed in the neonates of pregestational diabetic mothers.

Heart rate markers for prediction of fetal acidosis in an experimental study on fetal sheep

To overcome the difficulties in interpreting fetal heart rate (FHR), several tools based on the autonomic nervous system and heart rate variability (HRV) have been developed. The objective of this study was to use FHR and HRV parameters for the prediction of fetal hypoxia. It was an experimental study in the instrumented fetal sheep. Repeated umbilical cord occlusions were performed to achieve severe acidosis. Hemodynamic parameters, ECG, and blood gases were analyzed. The variables used were heart rate baseline, HRV analysis (RMSSD, SDNN, LF, HF, HFnu, Fetal Stress Index (FSI), …), and morphological analysis of decelerations. The gold standard used to classify hypoxia was the fetal arterial pH (pH < 7.10). Different multivariable statistical methods (logistic regression and decision trees) were applied for the detection of acidosis. 21 lambs were instrumented. A total of 130 pairs of FHR/fetal pH analysis were obtained of which 29 in the acidosis group and 101 in the non-acidosis group. After logistic regression model with bootstrap resampling and stepwise backward selection, only one variable was selected, FSI. The AUC of FSI alone in this model was 0.81 with a sensitivity of 0.66, specificity of 0.88, PPV of 0.61, and NPV of 0.90 considering a threshold of 68. Decision trees with CHAID and CART algorithms showed a sensitivity of 0.48 and 0.59, respectively, and a specificity of 0.94 for both. All employed methods identified HRV variables as the most predictive of acidosis. The primary variables selected automatically were those from the HRV. Supporting the use of FHRV measures for the screening of fetal acidosis during labour is interesting.

On designing a biosignal-based fetal state assessment system: A systematic mapping study

BACKGROUND AND OBJECTIVE

The patterns present in biosignals, such as fetal heart rate (FHR), are valuable indicators of fetal well-being. In designing biosignal analysis systems, the variety of approaches and technology usage impairs the decision-making for the fundamental units of the systems. There is a need for an updated overview of studies encompassing the biosignal-based fetal state assessment systems. Therefore, we propose a systematic mapping study to identify and synthesize recent research regarding the building blocks that compose these systems.

METHODS

We followed well-established guidelines to perform a systematic mapping of studies regarding the building-blocks that compose the fetal state assessment systems and published between January 2016 and January 2021. A search string was determined based on the mapping questions and the PI (population and intervention) divisions. The search string was applied in digital libraries covering the fields of computer science, engineering, and medical informatics. Then, we applied the forward snowballing technique to complement the resulting set. This process resulted in 75 primary studies selected from a total of 871 papers.

RESULTS

Selected studies were classified according to the publication types, systems design stages, datasets, and predictive capabilities. The results revealed that (i) The majority of the selected studies refer to the method as a type of publication and there is a lack of validation studies; (ii) The CTU-UHB was the most frequent biosignal-based dataset and UCI-CTG was the most frequent feature-based data; (iii) The selected studies made use of the system design stages alone or in a mixed-mode.

CONCLUSION

The results indicated that the well-established classification models achieved competitive results compared with the state-of-the-art methods in data-constrained system designs. Moreover, we identified the need for validation studies in the clinical environment.

Uterine activity modifies the response of the fetal autonomic nervous system at preterm active labor

BACKGROUND

The autonomic nervous system of preterm fetuses has a different level of maturity than term fetuses. Thus, their autonomic response to transient hypoxemia caused by uterine contractions in labor may differ. This study aims to compare the behavior of the fetal autonomic response to uterine contractions between preterm and term active labor using a novel time-frequency analysis of fetal heart rate variability (FHRV).

METHODS

We performed a case-control study using fetal R-R and uterine activity time series obtained by abdominal electrical recordings from 18 women in active preterm labor (32-36 weeks of gestation) and 19 in active term labor (39-40 weeks of gestation). We analyzed 20 minutes of the fetal R-R time series by applying a Continuous Wavelet Transform (CWT) to obtain frequency (HF, 0.2-1 Hz; LF, 0.05-0.2 Hz) and time-frequency (Flux0, Flux90, and Flux45) domain features. Time domain FHRV features (SDNN, RMSSD, meanNN) were also calculated. In addition, ultra-short FHRV analysis was performed by segmenting the fetal R-R time series according to episodes of the uterine contraction and quiescent periods.

RESULTS

No significant differences between preterm and term labor were found for FHRV features when calculated over 20 minutes. However, we found significant differences when segmenting between uterine contraction and quiescent periods. In the preterm group, the LF, Flux0, and Flux45 were higher during the average contraction episode compared with the average quiescent period (p<0.01), while in term fetuses, vagally mediated FHRV features (HF and RMSSD) were higher during the average contraction episode (p<0.05). The meanNN was lower during the strongest contraction in preterm fetuses compared to their consecutive quiescent period (p=0.008).

CONCLUSION

The average autonomic response to contractions in preterm fetuses shows sympathetic predominance, while term fetuses respond through parasympathetic activity. Comparison between groups during the strongest contraction showed a diminished fetal autonomic response in the preterm group. Thus, separating contraction and quiescent periods during labor allows for identifying differences in the autonomic nervous system cardiac regulation between preterm and term fetuses.

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

Roles of parasympathetic outflow and sympathetic outflow in the cardiovascular response to brief umbilical cord occlusion in fetal sheep

Fetal heart rate (FHR) deceleration is the most common change seen during labor. The role of the autonomic nervous system in regulating the fetal cardiovascular response during multiple uterine contractions has been well-established. However, the mechanism underlying the hemodynamic response remains unclear and the specific reflex that mediates the cardiovascular modifications is still controversial. This study aimed to determine the role of the sympathetic and parasympathetic systems on fetal hemodynamics in complete cord occlusion. Chronically instrumented fetal sheep were randomized to receive an intravenous injection of atropine 2.5 mg (n = 8), propranolol 5 mg (n = 7), atropine and propranolol (n = 7), or a control protocol (n = 9), followed by three episodes of 1-minute umbilical cord occlusion repeated every 5 minutes. Cord compression induces a rapid decrease in the FHR and a rapid increase in MAP. The decrease in FHR is caused by an increase in parasympathetic activity, (atropine and atropine-propranolol abolish the FHR response to the occlusion). The change in FHR during occlusion was not modified by propranolol injection, showing no effect of sympathetic tone. The increase in MAP during occlusion was similar in the four protocols. After releasing occlusion, the FHR was still lower than that at baseline due to a sustained parasympathetic tone. Suppression of the parasympathetic output to the cardiovascular system unmasks an increase in the FHR above baseline values. The lower FHR with the propranolol protocol further supports an increase in myocardial β-adrenoceptor stimulation after cord release. The increase in MAP after cord release was similar in the four protocols, except after the early stage of interocclusion period in atropine protocol. Four minutes after cord release, the FHR returned to baseline irrespective of the drugs that were infused, thereby showing recovery of ANS control. Blood gases (pH, PaCO2, PaO2) and plasma lactate concentrations was similar between the four protocols at the end of three applications of UCO. Complete cord compression-induced deceleration is likely due to acute activation of parasympathetic output. β-adrenoceptor activity is involved in the increase in FHR after cord release. Understanding the reflexes involved in FHR deceleration may help us understand the mechanisms underlying fetal autonomic adaptation during cord occlusion.

Opioid effect on the autonomic nervous system in a fetal sheep model

PURPOSE

Opioid use during labour can interfere with cardiotocography patterns. Heart rate variability indirectly reflects a fluctuation in the autonomic nervous system and can be monitored through time and spectral analyses. This experimental study aimed to evaluate the impact of nalbuphine administration on the gasometric, cardiovascular, and autonomic nervous system responses in fetal sheep.

METHODS

This was an experimental study on chronically instrumented sheep fetuses (surgery at 128 ± 2 days of gestational age, term = 145 days). The model was based on a maternal intravenous bolus injection of nalbuphine, a semisynthetic opioid used as an analgesic during delivery. Fetal gasometric parameters (pH, pO₂, pCO₂, and lactates), hemodynamic parameters (fetal heart rate and mean arterial pressure), and autonomic nervous system tone (short-term and long-term variation, low-frequency domain, high-frequency domain, and fetal stress index) were recorded. Data obtained at 30-60 min after nalbuphine injection were compared to those recorded at baseline.

RESULTS

Eleven experiments were performed. Fetal heart rate, mean arterial pressure, and activities at low and high frequencies were stable after injection. Short-term variation decreased at T30 min (P = 0.02), and long-term variation decreased at T60 min (P = 0.02). Fetal stress index gradually increased and reached significance at T60 min (P = 0.02). Fetal gasometric parameters and lactate levels remained stable.

CONCLUSION

Maternal nalbuphine use during labour may lead to fetal heart changes that are caused by the effect of opioid on the autonomic nervous system; these fluctuations do not reflect acidosis.

Systematic Review of Intrapartum Fetal Heart Rate Spectral Analysis and an Application in the Detection of Fetal Acidemia

It is fundamental to diagnose fetal acidemia as early as possible, allowing adequate obstetrical interventions to prevent brain damage or perinatal death. The visual analysis of cardiotocography traces has been complemented by computerized methods in order to overcome some of its limitations in the screening of fetal hypoxia/acidemia. Spectral analysis has been proposed by several studies exploring fetal heart rate recordings while referring to a great variety of frequency bands for integrating the power spectrum. In this paper, the main goal was to systematically review the spectral bands reported in intrapartum fetal heart rate studies and to evaluate their performance in detecting fetal acidemia/hypoxia. A total of 176 articles were reviewed, from MEDLINE, and 26 were included for the extraction of frequency bands and other relevant methodological information. An open-access fetal heart rate database was used, with recordings of the last half an hour of labor of 246 fetuses. Four different umbilical artery pH cutoffs were considered for fetuses' classification into acidemic or non-acidemic: 7.05, 7.10, 7.15, and 7.20. The area under the receiver operating characteristic curve (AUROC) was used to quantify the frequency bands' ability to distinguish acidemic fetuses. Bands referring to low frequencies, mainly associated with neural sympathetic activity, were the best at detecting acidemic fetuses, with the more severe definition (pH ≤ 7.05) attaining the highest values for the AUROC. This study shows that the power spectrum analysis of the fetal heart rate is a simple and powerful tool that may become an adjunctive method to CTG, helping healthcare professionals to accurately identify fetuses at risk of intrapartum hypoxia and to implement timely obstetrical interventions to reduce the incidence of related adverse perinatal outcomes.

Classifying the type of delivery from cardiotocographic signals: A machine learning approach

BACKGROUND AND OBJECTIVE

Cardiotocography (CTG) is the most employed methodology to monitor the foetus in the prenatal phase. Since the evaluation of CTG is often visual, and hence qualitative and too subjective, some automated methods have been introduced for its assessment.

METHODS

In this paper, a custom-made software is exploited to extract 17 features from the available CTG. A preliminary univariate statistical analysis is performed; then, five machine learning algorithms, exploiting ensemble learning, were implemented (J48, Random Forests (RF), Ada-boosting of decision tree (ADA-B), Gradient Boosting and Decorate) through Knime analytics platform to classify patients according to their delivery: vaginal or caesarean section. The dataset is composed by 370 signals collected between 2000 and 2009 in both public and private hospitals. The performance of the algorithms was evaluated using 10 folds cross validation with different evaluation metrics: accuracy, precision, sensitivity, specificity, area under the curve receiver operating characteristic (AUCROC).

RESULTS

While only two features were significantly different (gestation week and power expressed by the high frequency band of FHR power spectrum), from the statistical point of view, machine learning results were great. The RF obtained the best results: accuracy (91.1%), sensitivity (90.0%) and AUCROC (96.7%). The ADA-B achieved the highest precision (92.6%) and specificity (93.1%). As expected, the lowest scores were obtained by J48 that was the base classifier employed in all the others empowered implementations. Excluding the J48 results, the AUCROC of all the algorithms was greater than 94.9%.

CONCLUSION

In the light of the obtained results, that are greater than those ones found in the literature from comparable researches, it can be stated that the machine learning approach can actually help the physicians in their decision process when evaluating the foetal well-being.

Wearable Active Electrode for sEMG Monitoring Using Two-Channel Brass Dry Electrodes with Reduced Electronics

Gel-based electrodes are employed to record sEMG signals for prolonged periods. These signals are used for the control of myoelectric prostheses, clinical analysis, or sports medicine. However, when the gel dries, the electrode-skin impedance increases considerably. Using dry active electrodes (AEs) to compensate variations of impedance is an alternative for long-term recording. This work describes the optimization of the electronic design of a conventional AE by removing the impedance coupling stage and two filters. The proposed work consisted of 5 stages: electrodes, amplification (X250), 2.2 Vdc offset, low-pass filter, and ADC with USART communication. The device did not need the use of electrolytic gel. The measurements of CMRR (96 dB), amplitude of the output sEMG signal (∼1.6 Vp-p), and system bandwidth (15-450 Hz) were performed in order to confirm the reliability of the device as an sEMG signal acquisition system. The SNR values from seven movements performed by eleven volunteers were compared in order to measure the repeatability of the measurements (average 30.32 dB for a wrist flexion). The SNR for wrist flexion measured with the proposed and the commercial system was compared; the values were 49 dB and 60 dB, respectively.

Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-Transplant Patients

OBJECTIVES

In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effective means for dealing with uncertainties in the health decision-making process; therefore, FL-based CDSS becomes a very powerful tool for data and knowledge management, being able to think like an expert clinician. This work proposes an FL-based CDSS for the evaluation of renal function in posttransplant patients.

METHOD

Based on the data provided by the Department of Nephrology of the University Hospital Federico II of Naples, a statistical sample is selected according to appropriate inclusion criteria. Four fuzzy inference systems are implemented monitoring the renal function by the level of proteinuria and the glomerular filtration rate (GFR).

RESULTS

The systems show an accuracy of more than 90% and the outputs are provided through easy to read graphics, so that physicians can intuitively monitor the patient's clinical status, with the objective to improve drugs dosage and reduce medication errors.

CONCLUSIONS

We propose that the CDSSs for the assessment and follow-up of kidney-transplanted patients built in this study are applicable to clinical practice.

New Method for Beat-to-Beat Fetal Heart Rate Measurement Using Doppler Ultrasound Signal

The most commonly used method of fetal monitoring is based on heart activity analysis. Computer-aided fetal monitoring system enables extraction of clinically important information hidden for visual interpretation-the instantaneous fetal heart rate (FHR) variability. Today's fetal monitors are based on monitoring of mechanical activity of the fetal heart by means of Doppler ultrasound technique. The FHR is determined using autocorrelation methods, and thus it has a form of evenly spaced-every 250 ms-instantaneous measurements, where some of which are incorrect or duplicate. The parameters describing a beat-to-beat FHR variability calculated from such a signal show significant errors. The aim of our research was to develop new analysis methods that will both improve an accuracy of the FHR determination and provide FHR representation as time series of events. The study was carried out on simultaneously recorded (during labor) Doppler ultrasound signal and the reference direct fetal electrocardiogram Two subranges of Doppler bandwidths were separated to describe heart wall movements and valve motions. After reduction of signal complexity by determining the Doppler ultrasound envelope, the signal was analyzed to determine the FHR. The autocorrelation method supported by a trapezoidal prediction function was used. In the final stage, two different methods were developed to provide signal representation as time series of events: the first using correction of duplicate measurements and the second based on segmentation of instantaneous periodicity measurements. Thus, it ensured the mean heart interval measurement error of only 1.35 ms. In a case of beat-to-beat variability assessment the errors ranged from -1.9% to -10.1%. Comparing the obtained values to other published results clearly confirms that the new methods provides a higher accuracy of an interval measurement and a better reliability of the FHR variability estimation.

Fetal heart rate variability analysis for neonatal acidosis prediction

Fetal well-being during labor is usually assessed by visual analysis of a fetal heart rate (FHR) tracing. Our primary objective was to evaluate the ability of automated heart rate variability (HRV) analysis methods, including our new fetal stress index (FSI), to predict neonatal acidosis. 552 intrapartum recordings were analyzed. The analysis occurred in the last 90 min before birth and was conducted during two 5-min intervals: (i) a stable period of FHR and (ii) the period corresponding to the maximum FSI value. For each period, we computed the mean FHR, FSI, short-term variability (STV), and long-term variability (LTV). Visual FHR interpretation was performed using the FIGO classification. The population was separated into two groups: (i) an acidotic group with an arterial pH at birth ≤ 7.10 and a control group. Prediction of a neonatal pH ≤ 7.10 was assessed by computing the receiver-operating characteristic area under the curve (AUC). FHR, FSI, STV, and LTV did not differ significantly between groups during the stable period. During the FSI max peak period, LTV and STV correlated significantly in the acidotic group (- 5.85 ± 2.19, p = 0.010 and - 0.62 ± 0.29, p = 0.037, respectively). The AUC values were 0.569 for FIGO classification, 0.595 for STV, and 0.622 for LTV. The multivariate model (FIGO, FSI, FC, STV, LTV) had the greatest accuracy for predicting acidosis (AUC = 0.719). FSI was not predictive of neonatal acidosis probably because of the low quality of the FHR signal in cardiotocography. When used separately, HRV indexes and visual FHR analysis were poor predictors of neonatal acidosis. Including all indexes in a multivariate model increased the predictive ability.

Comparison of fetal heart rate variability by symbolic dynamics at the third trimester of pregnancy and low-risk parturition

Fetal heart rate variability (fHRV) is an essential source of information to monitor fetal well-being during pregnancy. This study aimed to apply a nonlinear approach, known as symbolic dynamics (SD), for comparing human fHRV in the third trimester of pregnancy during active fetal state (TT) and active labor at term (P). We performed a longitudinal, prospective, descriptive, and comparative study composed of 42 longitudinal recordings of 5-minutes of fetal heartbeat interval series. Recordings were collected from 21 low-risk, healthy, pregnant women attending the Maternal and Child Research Center (CIMIGen), Mexico City. We calculated relevant linear parameters of fHRV between TT and P stages, such as the percentage of differences between adjacent RR intervals >5 ms (PRR5, related to vagal modulations) and other SD parameters such as the percentage of no variations between three successive symbols (%0V, reflects sympathetic modulations) and the probability of low variability with a threshold of 4 ms (POLVAR4, associated with a low variability). We identified statistical differences for PRR5 between TT and P (37.13% [28.47-47.60%] vs. 28.84% [19.36-36.76%], p = 0.03), respectively. Also, for 0V% (65.66% [59.01-71.80%] vs. 71.14% [65.94-75.87%], p = 0.03) and for POLVAR4 values (0.06 [0.04-0.11] vs. 0.15 [0.09-0.24], p = 0.002), respectively. Our results indicate that during parturition, the short-term fetal fHRV is decreased, showing a decreased vagal modulations and higher adrenergic response of the heart. These autonomic modifications may result from the fetal response to the stressful inflammatory challenge of labor. We thus confirmed that the analysis of the SD applied to fHRV time series could be a potential clinical biomarker to differentiate the fetal autonomic cardiac condition at different stages of pregnancy.

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.

A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

Fetal cardiac examination can affect patients' preference on invasive tests: A new data on maternal anxiety indicated karyotyping

BACKGROUND

Prenatal screening for aneuploidies has seen great changes over the last 2 decades. But there is still no non-invasive diagnostic test. Therefore, prenatal invasive procedures are still being routinely performed due to maternal anxiety. The association of cardiac anomalies and abnormal findings with aneuploidies has been known for a long time. This prospective study was done to evaluate abnormal fetal cardiac examination (FCE) findings on patients undergoing diagnostic invasive procedures due to maternal anxiety and to assess the predictive value of abnormal cardiac findings on abnormal karyotype.

MATERIALS AND METHODS

Patients who underwent prenatal diagnostic invasive tests due to maternal anxiety indication between March 2013 and September 2016 were included in this study. FCE was performed in the study group immediately prior to invasive tests. Findings of fetal cardiac examination are classified as normal, major-minor cardiac anomalies and soft markers. Fetal karyotypes were compared among groups depending on cardiac findings.

RESULTS

One hundred eighty-two invasive procedures were performed because of maternal anxiety during this period. There were 29 abnormal findings detected on FCE. A total of 7 abnormal karyotypes were detected. FCE was abnormal in 5 of the abnormal karyotypes (71.4%). The presence of a major cardiac anomaly was most predictive for abnormal karyotype (LR+: 96,67, LR-: 0,34). No association was detected between the presence of minor cardiac anomalies and abnormal karyotype. Normal FCE appeared to be a good predictive factor for normal karyotype (LR-: 0.20).

CONCLUSIONS

This is the first study evaluating the power of early fetal cardiac examination findings on fetal aneuploidies. This study suggested that the application of fetal cardiac examination findings to genetic counseling for screening aneuploidies may be efficient on patients' preference about invasive tests. Due to the small number of abnormal findings and karyotypes detected (not the large study group), further studies on large study groups are needed to confirm these results.

Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment

Cardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decrease the rates of neonatal mortality and morbidity as well as unnecessary interventions. The analysis of CTG traces has become an indispensable part of present clinical practices; however, it also has serious drawbacks, such as poor specificity and variability in its interpretation. The automated CTG analysis is seen as the most promising way to overcome these disadvantages. In this study, a novel prognostic model is proposed for predicting fetal hypoxia from CTG traces based on an innovative approach called image-based time-frequency (IBTF) analysis comprised of a combination of short time Fourier transform (STFT) and gray level co-occurrence matrix (GLCM). More specifically, from a graphical representation of the fetal heart rate (FHR) signal, the spectrogram is obtained by using STFT. The spectrogram images are converted into 8-bit grayscale images, and IBTF features such as contrast, correlation, energy, and homogeneity are utilized for identifying FHR signals. At the final stage of the analysis, different subsets of the feature space are applied as the input to the least square support vector machine (LS-SVM) classifier to determine the most informative subset. For this particular purpose, the genetic algorithm is employed. The prognostic model was performed on the open-access intrapartum CTU-UHB CTG database. The sensitivity and specificity obtained using only conventional features were 57.33% and 67.24%, respectively, whereas the most effective results were achieved using a combination of conventional and IBTF features, with a sensitivity of 63.45% and a specificity of 65.88%. Conclusively, this study provides a new promising approach for feature extraction of FHR signals. In addition, the experimental outcomes showed that IBTF features provided an increase in the classification accuracy.

Autonomic nerve activity indexed using 24-h heart rate variability in patients with burns

BACKGROUND

Heart rate variability (HRV) is a noninvasive method used to quantify fluctuations in the time interval between normal heart beats. The purpose of this study was to compare the autonomic nervous system functioning of patients with burns to healthy participants after their burn scars had been re-epithelialized.

MATERIALS AND METHODS

The authors prospectively performed 24-h HRV monitoring in 60 patients with electrical burns, those with other major burns, those with other minor burns, and 10 healthy participants. Analysis of HRV in the time and frequency domain was performed.

RESULTS

The difference in sympathetic nerve measures (standard deviation of NN intervals [SDNN], total power [TP] and a low frequency [LF] band) and parasympathetic nerve measures (Root mean square successive difference [RMSSD], the number of interval differences of successive NN intervals greater than 50ms [NN50], the percentage of differences between following RR intervals greater than 50ms [pNN50] and a high frequency [HF] band) in patients with burns was significantly decreased during the daytime and the nighttime. the difference in parasympathetic nerve measures were more significantly decreased during the nighttime compared with measures of HRV in healthy participants. The groups of other burns showed significantly lower HRV than the electrical burn group indexed by a very low frequency (VLF) measure and TP during the daytime.

CONCLUSION

We hypothesized that HRV is a surrogate for autonomic nervous system dysfunction in patients with burns. The patients with burns were observed a sympathetic predominance during daytime and a decreased parasympathetic activity during nighttime. These results of patients with other major burns were more predominant compared with the results of patients with other groups.

Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces

Human visual inspection of Cardiotocography traces is used to monitor the foetus during labour and avoid neonatal mortality and morbidity. The problem, however, is that visual interpretation of Cardiotocography traces is subject to high inter and intra observer variability. Incorrect decisions, caused by miss-interpretation, can lead to adverse perinatal outcomes and in severe cases death. This study presents a review of human Cardiotocography trace interpretation and argues that machine learning, used as a decision support system by obstetricians and midwives, may provide an objective measure alongside normal practices. This will help to increase predictive capacity and reduce negative outcomes. A robust methodology is presented for feature set engineering using an open database comprising 552 intrapartum recordings. State-of-the-art in signal processing techniques is applied to raw Cardiotocography foetal heart rate traces to extract 13 features. Those with low discriminative capacity are removed using Recursive Feature Elimination. The dataset is imbalanced with significant differences between the prior probabilities of both normal deliveries and those delivered by caesarean section. This issue is addressed by oversampling the training instances using a synthetic minority oversampling technique to provide a balanced class distribution. Several simple, yet powerful, machine-learning algorithms are trained, using the feature set, and their performance is evaluated with real test data. The results are encouraging using an ensemble classifier comprising Fishers Linear Discriminant Analysis, Random Forest and Support Vector Machine classifiers, with 87% (95% Confidence Interval: 86%, 88%) for Sensitivity, 90% (95% CI: 89%, 91%) for Specificity, and 96% (95% CI: 96%, 97%) for the Area Under the Curve, with a 9% (95% CI: 9%, 10%) Mean Square Error.

Is Abdominal Fetal Electrocardiography an Alternative to Doppler Ultrasound for FHR Variability Evaluation?

Great expectations are connected with application of indirect fetal electrocardiography (FECG), especially for home telemonitoring of pregnancy. Evaluation of fetal heart rate (FHR) variability, when determined from FECG, uses the same criteria as for FHR signal acquired classically—through ultrasound Doppler method (US). Therefore, the equivalence of those two methods has to be confirmed, both in terms of recognizing classical FHR patterns: baseline, accelerations/decelerations (A/D), long-term variability (LTV), as well as evaluating the FHR variability with beat-to-beat accuracy—short-term variability (STV). The research material consisted of recordings collected from 60 patients in physiological and complicated pregnancy. The FHR signals of at least 30 min duration were acquired dually, using two systems for fetal and maternal monitoring, based on US and FECG methods. Recordings were retrospectively divided into normal (41) and abnormal (19) fetal outcome. The complex process of data synchronization and validation was performed. Obtained low level of the signal loss (4.5% for US and 1.8% for FECG method) enabled to perform both direct comparison of FHR signals, as well as indirect one—by using clinically relevant parameters. Direct comparison showed that there is no measurement bias between the acquisition methods, whereas the mean absolute difference, important for both visual and computer-aided signal analysis, was equal to 1.2 bpm. Such low differences do not affect the visual assessment of the FHR signal. However, in the indirect comparison the inconsistencies of several percent were noted. This mainly affects the acceleration (7.8%) and particularly deceleration (54%) patterns. In the signals acquired using the electrocardiography the obtained STV and LTV indices have shown significant overestimation by 10 and 50% respectively. It also turned out, that ability of clinical parameters to distinguish between normal and abnormal groups do not depend on the acquisition method. The obtained results prove that the abdominal FECG, considered as an alternative to the ultrasound approach, does not change the interpretation of the FHR signal, which was confirmed during both visual assessment and automated analysis.

Parasympathetic tone variations according to umbilical cord pH at birth: a computerized fetal heart rate variability analysis

Non-reassuring fetal heart rate tracings reflect an imbalance between the parasympathetic and sympathetic nervous systems. In this situation, fetal asphyxia can be suspected and may be confirmed by metabolic measurements at birth like low pH or high base deficit values. The objective of this study was to determine whether fetal asphyxia during labor is related to parasympathetic nervous system activity. This is a retrospective study of a database collected in 5 centers. Two hundred and ninety-nine fetal heart rate tracings collected during labor were analyzed. Autonomic nervous system, especially the parasympathetic nervous system, was analyzed using an original index: the FSI (Fetal Stress Index). The FSI is a parasympathetic activity evaluation based on fetal heart rate variability analysis. Infants were grouped based on normal or low pH value at birth. FSI was measured during the last 30 min of labor before birth and compared between groups. The minimum value of the FSI during the last 30 min before delivery was significantly lower in the group with the lower umbilical cord arterial pH value. In this pilot study during labor, FSI was lower in the group of infants with low arterial pH at birth.

Fetal Heart Rate Monitoring from Phonocardiograph Signal Using Repetition Frequency of Heart Sounds

As a passive, harmless, and low-cost diagnosis tool, fetal heart rate (FHR) monitoring based on fetal phonocardiography (fPCG) signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected heart sound bursts. However, the performance is unavoidable to degrade due to missed heart sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of heart sounds. The proposed method can track time-varying heart rate without both heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4 dB to −26.7 dB.