Robert Czabanski

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

Abstract 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.

Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia

Cardiotocography is the primary method for biophysical assessment of fetal state, which is mainly based on the recording and analysis of fetal heart rate (FHR) signal. Computerized systems for fetal monitoring provide a quantitative analysis of FHR signals, however the effective methods of qualitative assessment that could support the process of medical diagnosis are still needed. The measurements of hydronium ions concentration (pH) in neonatal cord blood are an objective indicator of the fetal outcome. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a two-step analysis of fetal heart rate recordings that allows for effective prediction of the acidemia risk. The first step consists in fuzzy classification of FHR signals. Fuzzy inference corresponds to the clinical interpretation of signals based on the FIGO guidelines. The goal of inference is to eliminate recordings indicating the fetal wellbeing from the further classification process. In the second step, the remained recordings are nonlinearly classified using multilayer perceptron and Lagrangian Support Vector Machines (LSVM). The proposed procedures are evaluated using data collected with computerized fetal surveillance system. The assessment performance is evaluated with the number of correct classifications (CC) and quality index (QI) defined as the geometric mean of sensitivity and specificity. The highest CC=92.0% and QI=88.2% were achieved for the Weighted Fuzzy Scoring System combined with the LSVM algorithm. The obtained results confirm the efficacy of the proposed methods of computerized analysis of FHR signals in the evaluation of the risk of neonatal acidemia.

Predicting the Risk of Low-Fetal Birth Weight From Cardiotocographic Signals Using ANBLIR System With Deterministic Annealing and

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 ε-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 ε-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.

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Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces is based on the visual analysis of patterns describing the variability of fetal heart rate signal. The correct interpretation of traces from a bedside monitor is rather difficult even for experienced clinicians, so computer-aided fetal monitoring systems have become very popular. At present effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The presented work describes an application the Artificial Neural Network Based on Logical Interpretation of fuzzy if-then Rules (ANBLIR) to classification of the fetal state as being normal or abnormal. A set of quantitative parameters describing fetal cardiotocograms is the system input. To evaluate the quality of the classification we proposed the overall validity index as a function of various prognostic indices. The obtained results confirm the usability of the ANBLIR neuro-fuzzy system for records classification within computer-aided fetal surveillance systems.

-Insensitive Learning

The electrooculogram represents the electrical activity of muscles which steering of movements of an eye. The eye blinking is a natural protection system which defends the eye from environmental exposure. The spontaneous eye blink is considered to be a suitable indicator for fatigue diagnostics during many, different tasks of human being activity. The detection function is used to detect the spontaneous eye blink action. On this base the position of an eye blink is estimated. The results demonstrate that the measurement of an eye blink parameter provides reliable information for eye-controlled systems from human-machine interface.

A Neuro-Fuzzy Approach to the Classification of Fetal Cardiotocograms

In this paper a new method of parameters estimation for neuro-fuzzy system with parameterized consequents is presented. The novelty of the learning algorithm consists of an application of the deterministic annealing method integrated with e-insensitive learning. This method allows to improve neuro-fuzzy modeling quality in the sense of an increase in generalization ability and outliers robustness. To demonstrate performance of the proposed procedure two numerical experiments concerning benchmark problems of prediction and identification are given.

An application of detection function for the eye blinking detection

The telemonitoring problem of high-risk pregnancies at home is introduced, and some design issues of the monitoring system are identified. A Medical Cyber-Physical System (MCPS) approach has been taken. Various MCPS design issues and requirements, like: interaction of caregivers and a patient with the MCPS system, Plug-and-Play architecture, maintenance support for caregivers, interoperability of medical devices, medical workflows automation, dependability of the system, smart alerting, and intelligent acquisition of biosignals, have been addressed. The telecare system consists of the Body Area Network (BAN) of advanced sensors that are interconnected on a body of a pregnant woman, the Personal Area Network (PAN) that is responsible for embedded processing of physical signals, smart alerting, an intelligent human-machine-interface, and a reliable transmission channel to the Surveillance Centre located in a hospital or a local medical centre. The system integrates the new strategy for abdominal signal acquisition and analysis based on the smart selection of algorithms realized in the mobile instrumentation of PAN. Dependable telemedical systems, when broadly deployed, will provide a high societal value to high-risk pregnant women, especially those in dispersed rural areas.

Deterministic annealing integrated withε-insensitive learning in neuro-fuzzy systems

Fuzzy clustering is often applied to determine the rules of the fuzzy rule-based classifiers (usually the antecedents only). In this work a new fuzzy clustering approach is proposed for such a purpose. The idea consists in alternating clustering of the objects from two classes with the prototypes obtained after the previous clustering not allowed to move during the current clustering. As a result each clustering provides new location of a single prototype. The classification quality obtained by the fuzzy rule-based classifier using the proposed clustering was compared with the Lagrangian SVM method on several benchmark databases.

Selected design issues of the medical cyber-physical system for telemonitoring pregnancy at home

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.

Classification Based on Incremental Fuzzy \((1+p)\)-Means Clustering

ABSTRACT Cardiotocographic (CTG) monitoring, consisting in analysis of the fetal heart rate, uterine contractions, and fetal movements is the primary noninvasive method for the fetal state assessment. The visual interpretation of the CTG signals is characterized by the large inter- and intraobserver disagreement. Hence, the automated methods supporting the diagnosis process are the topic of researches. In the presented study, the evaluation of the CTG signals, based on fuzzy clustering with pairs of prototypes, is described. The efficiency of the proposed method is verified using two benchmark datasets of the CTG signals (CTU-UHB and SisPorto), and the problems of the two- and three-class classification are considered. The obtained results show the improved quality of the automated fetal state assessment in accordance with the applied reference procedures: the fuzzy (c+p)-means clustering and the Lagrangian Support Vector Machines.