A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction.

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values µ < 0.1 and values of ±1.96σ < 0.1).

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 electrocardiograms, direct and abdominal with reference heartbeat annotations.

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.

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%.

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring.

Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01-real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.

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.

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 µ 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.

Application of fuzzy inference systems for classification of fetal heart rate tracings in relation to neonatal outcome.

OBJECTIVES: Fetal monitoring based on the analysis of the fetal heart rate (FHR) signal is the most common method of biophysical assessment of fetal condition during pregnancy and labor Visual analysis of FHR signals presents a challenge due to a complex shape of the waveforms. Therefore, computer-aided fetal monitoring systems provide a number of parameters that are the result of the quantitative analysis of the registered signals. These parameters are the basis for a qualitative assessment of the fetal condition. The guidelines for the interpretation of FHR provided by FIGO are commonly used in clinical practice. On their basis a weighted fuzzy scoring system was constructed to assess the FHR tracings using the same criteria as those applied by expert clinicians. The effectiveness of the automated classification was evaluated in relation to the fetal outcome assessed by Apgar score. MATERIAL AND METHODS: The proposed automated system for fuzzy classification is an extension of the scoring systems used for qualitative evaluation of the FHR tracings. A single fuzzy rule of the system corresponds to a single evaluation principle of a signal parameter derived from the FIGO guidelines. The inputs of the fuzzy system are the values of quantitative parameters of the FHR signal, whereas the system output, which is calculated in the process of fuzzy inference, defines the interpretation of the FHR tracing. The fuzzy evaluation process is a kind of diagnostic test, giving a negative or a positive result that can be compared with the fetal outcome assessment. The present retrospective study included a set of 2124 one-hour antenatal FHR tracings derived from 333 patients, recorded between 24 and 44 weeks of gestation (mean gestational age: 36 weeks). Various approaches for the research data analysis, depending on the method of interpretation of the individual patient-tracing relation, were used in the investigation. The quality of the fuzzy analysis was defined by the number of correct classifications (CC) and the additional index QI - the geometric mean of the sensitivity and specificity values. RESULTS: The effectiveness of the fetal assessment varied, depending on the assumed relation between a patient and a set of her tracings. The approach, based on a common assessment of the whole set of tracings recorded for a single patient, provided the highest quality of automated classification. The best results (CC = 70.9% and QI = 84.0%) confirmed the possibility of predicting the neonatal outcome using the proposed fuzzy system based on the FIGO guidelines. CONCLUSIONS: It is possible to enhance the process of the fetal condition assessment with classification of the FHR records through the implementation of the heuristic rules of inference in the fuzzy signal processing algorithms.

Determination of fetal heart rate from abdominal signals: evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram.

The main aim of our work was to assess the reliability of indirect abdominal electrocardiography as an alternative to the commonly used Doppler ultrasound monitoring technique. As a reference method, we used direct fetal electrocardiography. Direct and abdominal signals were acquired simultaneously, using dedicated instrumentation. The developed method of maternal signal suppression as well as fetal QRS complexes detection was presented. Recordings were collected during established labors, each consisted of four signals from the maternal abdomen and the reference signal acquired directly from the fetal head. After assessing the performance of the QRS detector, the accuracy of fetal heart rate measurement was evaluated. Additionally, to reduce the influence of inaccurately detected R-waves, some validation rules were proposed. The obtained results revealed that the indirect method is able to provide an accuracy sufficient for a reliable assessment of fetal heart rate variability. However, the method is very sensitive to recording conditions, influencing the quality of signals. Our investigations confirmed that abdominal electrocardiography, even in its current stage of development, offers an accuracy equal to or higher than an ultrasound method, at the same time providing some additional features.

A new method of saccadic eye movement detection for optokinetic nystagmus analysis.

The analysis of eye movements is valuable in both clinical work and research. One of the characteristic type of eye movements is saccade. The accurate detection of saccadic eye movements is the base for further processing of saccade parameters such as velocity, amplitude and duration. This paper concerns an accurate saccade detection method that is based on pre-processing signal and then the proposed non-linear detection function can be applied. The described method characterizes less sensitivity for any kind of noise due to an application of the robust myriad filter which is used to eliminate baseline drifts and impulsive artifacts. The congenital nystagmus is one of the field where our method can be applied to detect saccades. The proposed detection function is computationally efficient and precisely determines the time position of saccadic eye movements even when the signal-to-noise ratio is low. The presented method may have potential application in automatic ENG signal processing systems for determining visual acuity.

A novel technique for fetal heart rate estimation from Doppler ultrasound signal.

BACKGROUND: The currently used fetal monitoring instrumentation that is based on Doppler ultrasound technique provides the fetal heart rate (FHR) signal with limited accuracy. It is particularly noticeable as significant decrease of clinically important feature - the variability of FHR signal. The aim of our work was to develop a novel efficient technique for processing of the ultrasound signal, which could estimate the cardiac cycle duration with accuracy comparable to a direct electrocardiography. METHODS: We have proposed a new technique which provides the true beat-to-beat values of the FHR signal through multiple measurement of a given cardiac cycle in the ultrasound signal. The method consists in three steps: the dynamic adjustment of autocorrelation window, the adaptive autocorrelation peak detection and determination of beat-to-beat intervals. The estimated fetal heart rate values and calculated indices describing variability of FHR, were compared to the reference data obtained from the direct fetal electrocardiogram, as well as to another method for FHR estimation. RESULTS: The results revealed that our method increases the accuracy in comparison to currently used fetal monitoring instrumentation, and thus enables to calculate reliable parameters describing the variability of FHR. Relating these results to the other method for FHR estimation we showed that in our approach a much lower number of measured cardiac cycles was rejected as being invalid. CONCLUSIONS: The proposed method for fetal heart rate determination on a beat-to-beat basis offers a high accuracy of the heart interval measurement enabling reliable quantitative assessment of the FHR variability, at the same time reducing the number of invalid cardiac cycle measurements.

Predicting the risk of low-fetal birth weight from cardiotocographic signals using ANBLIR system with deterministic annealing and epsilon-insensitive learning.

Cardiotocography (CTG) is a biophysical method of fetal condition assessment based mainly on recording and automated analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the CTG signals, but the effective conclusion generation methods for decision process support are still needed. Assessment of the fetal state can be verified only after delivery using the fetal (newborn) outcome data. One of the most important features defining the abnormal fetal outcome is low birth weight. This paper describes an application of the artificial neural network based on logical interpretation of fuzzy if-then rules neurofuzzy system to evaluate the risk of low-fetal birth weight using the quantitative description of CTG signals. We applied different learning procedures integrating least squares method, deterministic annealing (DA) algorithm, and epsilon-insensitive learning, as well as various methods of input dataset modification. The performance was evaluated with the number of correctly classified cases (CC) expressed as the percentage of the testing set size, and with overall index (OI) being the function of predictive indexes. The best classification efficiency (CC = 97.5% and OI = 82.7%), was achieved for integrated DA with epsilon-insensitive learning and dataset comprising of the CTG traces recorded as earliest for a given patient. The obtained results confirm efficiency for supporting the fetal outcome prediction using the proposed methods.

[Prognostic value of chosen parameters of mechanical and bioelectrical uterine activity in prediction of threatening preterm labour]. / Wartosc wybranych parametrów mechanicznej i bioelektrycznej aktywnosci miesnia macicy w przewidywaniu zagrozenia porodem przedwczesnym.

OBJECTIVES: To record and analyse bioelectrical activity of the uterine muscle in the course of physiological pregnancy, labour and threatening premature labour; to define which parameters from the analysis of both electrohysterogram and mechanical activity signal allow us to predict threatening premature labour. MATERIAL AND METHODS: Material comprised 62 pregnant women: Group I--27 patients in their first physiological pregnancy, Group II--21 patients in their first pregnancy with symptoms of threatening premature labour, and Group III--14 patients in the first labour period. The on-line analysis of the mechanical (TOCO) and electrical (EHG) contraction activity relied on determination of quantitative parameters of detected uterine contractions. RESULTS: The obtained statistical results demonstrated a possibility to differentiate between Group I and II through the amplitude and contraction area for EHG signal, and only the contraction amplitude for TOCO signal. Additionally, significant differentiating parameters for electrohysterogram are: contraction power and its median frequency. Analyzing Group I and III, significant differences were noted for contraction amplitude and area obtained both from EHG and TOCO signals. Similarly, the contraction power (from EHG) enables us to assign the contractions either to records from Group I or to labour type. There was no significant difference noted between Group II and III. CONCLUSIONS: Identification of pregnant women at risk of premature labour should lead to their inclusion in rigorous perinatal surveillance. This requires novel, more sensitive methods that are able to detect early symptoms of the uterine contraction activity increase. Electrohysterography provides complete information on principles of bioelectrical uterine activity. Quantitative parameters of EHG analysis enable the detection of records (contractions) with the symptoms of premature uterine contraction activity.

Extraction of fetal heart-rate signal as the time event series from evenly sampled data acquired using Doppler ultrasound technique.

Analysis of variability of fetal heart rate (FHR) is very important in prediction of the fetal wellbeing. The beat-to-beat variability is described quantitatively by the indices originated from invasive fetal electrocardiography which provides the FHR signal in a form of time event series. Today, monitoring instrumentation is based on Doppler ultrasound technology. We used two bedside fetal monitors with different processing methods for heartbeat detection and FHR signal determination: the autocorrelation and cross-correlation techniques. Both monitors provide the output signal in a form of evenly spaced samples. The goal of this paper is to present a new method for the FHR signal processing, which enables extraction of series of consecutive heartbeat intervals from the sampled signal. The proposed correction algorithms allow recognition and removal of the FHR signal distortions typical for fetal monitors--invalid and duplicated samples. The correction efficiency has been verified based on the FHR variability indices calculated for the sampled signal and the corresponding event series. For both monitors, considerable influence of the signal representation on indices values was noted. Concluding, we recommended implementing these algorithms in fetal surveillance system as a preprocessing stage for the determination of FHR variability indices.

Detection of low amplitude fetal QRS complexes.

The significance of the most important operations performed by the noninvasive systems for fetal heart rate determination is investigated. The method of template subtraction for maternal ECG suppression is compared to the method based on independent component analysis. The QRS detector based on the classical slope-responsive preprocessing competes with the one that employs normalized matched filtering for QRS enhancement. A small database containing the four-channel abdominal ECG signals with the marked positions of the fetal QRS complexes is prepared to enable the investigations. The performed experiments show the factors that have the greatest impact on the results of the fetal QRS detection, and an effective approach to cope the problem is proposed.

[Mechanical and electrical uterine activity. Part I. Contractions monitoring]. / Aktywnosc mechaniczna i elektryczna macicy. Czesc I. Monitorowanie skurczów.

Correct uterine contraction activity during labour determines physiological fetal delivery and ensures its satisfactory outcome. Contraction activity monitoring may be accomplished by either recording of the mechanical properties of the uterine muscle and/or by measurement of the action potentials produced by the uterus during contraction. In the following paper, the current state of knowledge concerning the methods for assessment and monitoring of the uterine contraction activity was evaluated. The electrophysiological properties of the uterus were given. The mechanical methods of uterine activity monitoring: internal and external tocography were described. The development of the electrohysterography as the method providing the signal comprising complete information on bioelectrical properties of the uterine muscle was presented. The conclusion was that the analysis of the electrohysterogram enables a description of the source of the uterine contraction activity, whereas currently applied mechanical methods merely record the results of this activity.

[Mechanical and electrical uterine activity. Part II. Contractions parameters]. / Aktywnosc mechaniczna i elektryczna macicy. Czesc II. Parametry skurczów.

Frequency and strength of the uterine contractions monitoring enables to control the labour progress and also, although in a restricted way, to determine the beginning of labour, as long as it is not preterm. Mechanical approach provides only the low frequency signal, which describes the contractions more or less accurately, depending on whether an intrauterine pressure measurement is used in the former case or whether an external stress measurement is applied in the latter case. This signal does not comprise information on contractions characteristics and enables only to estimate their basic timing parameters. Description of the electrophysiological properties may be obtained only by means of the uterine electrical signals measurement. In the following paper, the classical interpretation of the uterine contraction activity which relies upon its mechanical and electrical activity was presented. Additionally, the frequency parameters provided exclusively by the electrical signal were proposed. The possibility of the electrohysterogram analysis may provide more complete information on uterine muscle functioning. Results of the research studies show that further development of electrohysterography will enable its wider application in pregnancy and labour diagnostics.

New approach to quantitative description of deceleration of fetal heart rate for the patterns classification.

The most common method of biophysical fetal monitoring is recording and analyzing the cardiotocographic signals. In analysis of the fetal heart rate signal special emphasis is paid to the deceleration patterns and their correlation to the uterine contractions. According to deceleration classification the most important is the distinguishing between the periodic and the episodic types. In visual analysis, this classification is based on fuzzy description of deceleration onset being "abrupt" or "gradual". Application of commonly used interpretation of these imprecise terms in computer aided monitoring systems very often leads to erroneous classifications. Therefore, the redefinition of the deceleration nadir phase, as a group of samples around the lowest point, is required. It ensures that the onset phase, which is very important in deceleration classification, will consist of only appropriate samples. For determination of nadir the new method based on three stage-analysis of samples frequency distribution was developed. To evaluate the proposed method we compared the results with reference data obtained from clinical experts.

Some practical remarks on neural networks approach to fetal cardiotocograms classification.

Cardiotocographic monitoring is a primary biophysical method for assessment of a fetal state based on quantitative analysis of the biophysical signals. Although the computerized fetal monitoring systems have become a standard in clinical centres, the effective methods, which could enable conclusion generation, are still being searched. In the proposed work the attempts have been made to answer some important questions, which occurred during application of neural network for classification of the fetal state as being normal or abnormal. These questions are particularly important for medical applications and concern the influence of data set organization, inputs representation and the network's architecture. The networks of MLP and RBF types were developed and tested using 50 trials, with randomly mixed data contents in learning, validating and testing subsets. Additionally, the influence of numerical and categorical representation of the input quantitative parameters describing fetal cardiotocograms on the efficiency of the learning process was tested.

The influence of coincidence of fetal and maternal QRS complexes on fetal heart rate reliability.

Bioelectrical fetal heart activity being recorded from maternal abdominal surface contains more information than mechanical heart activity measurement based on the Doppler ultrasound signals. However, it requires extraction of fetal electrocardiogram from abdominal signals where the maternal electrocardiogram is dominant. The simplest technique for maternal component suppression is a blanking procedure, which relies upon the replacement of maternal QRS complexes by isoline values. Although, in case of coincidence of fetal and maternal QRS complexes, it causes a loss of information on fetal heart activity. Its influence on determination of fetal heart rate and the variability analysis depends on the sensitivity of the heart-beat detector used. The sensitivity is defined as an ability to detect the incomplete fetal QRS complex. The aim of this work was to evaluate the influence of the maternal electrocardiogram suppression method used on the reliability of FHR signal being calculated.