Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Mathematical tools for identifying the fetal response to physical exercise during pregnancy

In the applied mathematics literature there exist a significant number of tools that can reveal the interaction between mother and fetus during rest and also during and after exercise. These tools are based on techniques from a number of areas such as signal processing, time series analysis, neural networks, heart rate variability as well as dynamical systems and chaos. We will briefly review here some of these methods, concentrating on a method of extracting the fetal heart rate from the mixed maternal-fetal heart rate signal, that is based on phase space reconstructio

Extraction Of Fetal Electrocardiogram Using An Adaptive Neuro-Fuzzy System

In this paper, adaptive neuro fuzzy inference system (ANFIS) was used for the cancellation of maternal electrocardiogram (MECG) in fetal electrocardiogram extraction (FECG) from the composite abdominal electrocardiogram (AECG). This technique is used to estimate the MECG present in the abdominal signal of a pregnant woman. The FECG is then extracted by subtracting the estimated MECG from the abdominal signal. In the furtherance of extraction, MATLAB (version 7.6) was used to code the system in order to generate the maternal heartbeat signal and the fetal heartbeat signal which were added to form the measured signal. For the fetal heartbeat signal to be recovered from the interference (maternal heartbeat) signal, a reference signal (which is a clean version of the original maternal heartbeat signal) was introduced in the system. It is this signal that cancelled the maternal heartbeat signal in the measured signal, thereby leaving the fetal heartbeat signal as an error signal. However, though the recovered signal still contained some traces of the maternal heartbeat signal, performance of the soft computing technique applied is in terms of the capability of adaptive neuro fuzzy inference system in removing the overlapping between the MECG and the FECG signals. The results obtained show that this method is a simple and powerful means for the extraction of Fetal Electrocardiogram.   Keywords: Fetal Electrocardiogram Extraction (FECG), Neuro-fuzzy system, Noise Cancellatio

Nonlinear Adaptive Signal Processing Improves the Diagnostic Quality of Transabdominal Fetal Electrocardiography

The abdominal fetal electrocardiogram (fECG) conveys valuable information that can aid clinicians with the diagnosis and monitoring of a potentially at risk fetus during pregnancy and in childbirth. This chapter primarily focuses on noninvasive (external and indirect) transabdominal fECG monitoring. Even though it is the preferred monitoring method, unlike its classical invasive (internal and direct) counterpart (transvaginal monitoring), it may be contaminated by a variety of undesirable signals that deteriorate its quality and reduce its value in reliable detection of hypoxic conditions in the fetus. A stronger maternal electrocardiogram (the mECG signal) along with technical and biological artifacts constitutes the main interfering signal components that diminish the diagnostic quality of the transabdominal fECG recordings. Currently, transabdominal fECG monitoring relies solely on the determination of the fetus’ pulse or heart rate (FHR) by detecting RR intervals and does not take into account the morphology and duration of the fECG waves (P, QRS, T), intervals, and segments, which collectively convey very useful diagnostic information in adult cardiology. The main reason for the exclusion of these valuable pieces of information in the determination of the fetus’ status from clinical practice is the fact that there are no sufficiently reliable and well-proven techniques for accurate extraction of fECG signals and robust derivation of these informative features. To address this shortcoming in fetal cardiology, we focus on adaptive signal processing methods and pay particular attention to nonlinear approaches that carry great promise in improving the quality of transabdominal fECG monitoring and consequently impacting fetal cardiology in clinical practice. Our investigation and experimental results by using clinical-quality synthetic data generated by our novel fECG signal generator suggest that adaptive neuro-fuzzy inference systems could produce a significant advancement in fetal monitoring during pregnancy and childbirth. The possibility of using a single device to leverage two advanced methods of fetal monitoring, namely noninvasive cardiotocography (CTG) and ST segment analysis (STAN) simultaneously, to detect fetal hypoxic conditions is very promising

Fetal Electrocardiogram Signal Extraction by ANFIS Trained with PSO Method

Studies indicate that the primary source of distress in pregnent mothers is their concerns about fetus’s condition and health. One way to know about condition of fetus is non-invasive fetal electrocardiogram signal extraction through which the components of fetal electrocardiogram signal are extracted from a signal recorded at abdominal area of mother which is a combination of fetal and maternal electrocardiogram signal and noise source components. The purpose of this study is to propose an algorithm to boost this extraction. To this end, we decomposed electrocardiogram signal to its Intrinsic Mode Functions (IMFs) thruogh Empirical Mode Decomposition algorithm; then, we removed the last and collected the other IMFs to reconstruct electrocardiogram signal without Baseline. Afterwards, we used Particle Swarm Optimization to train and adjust the parameters of Adaptive Neuro-Fuzzy Inference System to model the path that maternal electrocardiogram signal travel to reach abdominal area. Accordingly, we were able to distinguish and remove maternal electrocardiogram signal components from the recorded signal and hence we obtained a good approximation of fetal electrocardiogram signal. We implemented our algorithm and other algorithms on simulated and real signals and found out that, in most cases, the proposed algorithm improved the extraction of fetal electrocardiogram signal.DOI:http://dx.doi.org/10.11591/ijece.v2i2.23

Detection Of Fetal Electrocardiogram from Multivariate Abdominal Recordings by using Wavelets and Neuro-Fuzzy Systems

The fetal electrocardiogram (FECG) signal reflects the electrical activity of the fetal heart. It contains  information  on  the health  status of the fetus and therefore, an early diagnosis of any cardiac defects before delivery (Specially in case of  labour pain) increases the effectiveness of the appropriate treatment. In this paper we consider one signal from the thoracic and another from abdomen of the mother. The artificial neural network fuzzy inference system (ANFIS) is used for estimating the FECG component from one abdominal ECG recording and one reference thoracic maternal electrocardiogram (MECG) signal. The obtained FECG is being enhanced by using wavelet transform. Key words: ECG, MECG, FECG, Neural network , Fuzzy logic, Membership function and Wavelet transform

Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size mu and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.Web of Science175art. no. 115

Using Adaptive Neuro-fuzzy Inference System in Processing of Fetal ECG

Tato práce je zaměřena na využití adaptivního neuro-fuzzy inferenčního systému (ANFIS) ve zpracování plodového EKG. v první části je nastíněna problematika plodového EKG. Dále jsou popsány soft-computingové metody jako je fuzzy logika, fuzzy inferenční systém a umělé neuronové sítě. V další části jsou uvedeny adaptivní metody zpracování signálu a samotná implementace softwarového rozhraní využívající adaptivní neuro-fuzzy inferenční systém. V poslední části se přistupuje k testování tohoto systému na různých vstupních signálech a k závěrečnému zhodnocení prováděných experimentů.This thesis is focused on the use of the adaptive neuro-fuzzy inference system (ANFIS) in processing of the fetal ECG. In the firts part problem of fetal ECG is outlined. There ase also described soft-computing methods such as fuzzy logic, fuzzy inference system a artificial neural networks. In the next part are presented the adaptive signal processing methods and the implementation of the software interface using the adaptive neuro-fuzzy inference system. In the last part, the system is tested on different input signals and the final evaluation of experiments.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

INTELLIGENT TECHNIQUE OF CANCELING MATERNAL ECG IN FECG EXTRACTION

ABSTRACT. In this paper, we propose a technique of artificial intelligence called adaptive neuro fuzzy inference system (ANFIS) for canceling maternal electrocardiogram (MECG) in fetal electrocardiogram extraction (FECG).This technique is used to estimate the MECG present in the abdominal signal of a pregnant woman. The FECG is then extracted by subtracting the estimated MECG from the abdominal signal. Performance of the proposed method in terms of mean square error, signal to noise ratio is compared with neural network. Our results show that this method is a simple and powerful means for the extraction of FECG