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

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 phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89

Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

Fetal Heart Rate Fragmentation

This article was supported by National Funds through FCT– Fundação para a Ciência e a Tecnologia, I.P., within CINTESIS, R&D Unit (reference UIDB/4255/2020)info:eu-repo/semantics/publishedVersio

Linear and nonlinear heart-rate analysis in a rat model of acute anoxia

The objective of this study was the assessment of heart-rate (HR) dynamics with linear and nonlinear methods during episodes of mechanical ventilation and acute anoxia in rats. Namely, to assess whether linear and nonlinear HR analysis was able to discriminate acute anoxia from baseline in rats and if this was consistent with human foetal and adult studies. Five HR segments of 1 min duration, during baseline recording, mechanical ventilation and first, second and third minutes of induced acute anoxia, were analysed in ten adult Wistar rats. Linear time and frequency domain and nonlinear methods were used, namely mean HR (mHR), long-term irregularity (LTI), interval index (II), low frequency (LF) and high frequency (HF), approximate entropy (ApEn) and sample entropy (SampEn). New parameters for the entropy indices are proposed for the analysis of rats' HR. Bootstrap percentile confidence intervals and nonparametric statistical tests were used in the evaluation of the differences between segments. During mechanical ventilation a clear spectral band was detectable at the ventilation rate, but mHR, II and the 'new' entropy indices were the only significantly changed indices. In the transition from baseline - mechanical-ventilation to mechanical-ventilation induced anoxia, a statistically significant decrease of mHR, II and entropy indices was observed, clearly discriminating these two instances, whereas most linear indices increased. With continued anoxia, most linear indices decreased significantly, whereas entropy remained stably low. These results are consistent with other foetal human and non-human studies and evidence that the rat model may be used for further research on linear and nonlinear analysis of heart-rate dynamics. The transition from baseline to acute anoxia was encompassed by signs of increased activation of the autonomic nervous system sympathetic branch, and decreased or blunted activity of the HR complexity regulatory centres

A novel approach for cardiotocography paper digitization and classification for abnormality detection

Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced