A method of ensuring data integrity in a data stream management system

Assurance of data integrity is one of the prerequisites for each computer system. The paper presents a method enabling on-line maintenance of stream data set integrity. This method is implemented in a data stream management system prototype designed to find application in a biomedical monitoring system. In the case of medical computer systems assurance of data integrity is particularly important for documenting formal results and for the patient's safety

Generalized fuzzy clustering method

This paper presents a new hybrid fuzzy clustering method. In the proposed method, cluster prototypes are values that minimize the introduced generalized cost function. The proposed method can be considered as a generalization of fuzzy c–means (FCM) method as well as the fuzzy c–median (FCMed) clustering method. The generalization of the cluster cost function is made by applying the Lp norm. The values that minimize the proposed cost function have been chosen as the group prototypes. The weighted myriad is the special case of the group prototype, when the Lp norm is the L2 (Euclidean) norm. The cluster prototypes are the weighted meridians for the L1 norm. Artificial data set is used to demonstrate the performance of proposed method

State-space averaging for maternal ECG suppression

In this paper a new method of maternal electrocardiogram suppression for fetal component extraction from one-channel maternal abdominal bioelectric signals is proposed. The method performs maternal ECG estimation by application of state-space averaging. The estimated signal is subtracted from the original one and this way suppressed. The method parameters allow us to balance between the precision of maternal ECG suppression and the necessity not to attenuate the fetal QRS complexes. A small database of the maternal abdominal bioelectric signals is used to investigate the developed system for fetal heart rate determination. The final assessment is based on the detection performance index. It is shown that by proper choice of the parameters we can tune the system so that it is more effective than the classical approach based on template subtraction

Separation of abdominal fetal electrocardiograms in twin pregnancy

A combined application of independent component analysis and projective filtering of the time-aligned ECG beats is proposed to solve the problem of fetal ECG extraction from multi-channel maternal abdominal electric signals. The developed method is employed to process the four-channel abdominal signals recorded during twin pregnancy. The signals are complicated mixtures of the maternal ECG, the ECGs of the fetal twins and noise of other origin. The independent component analysis cannot separate the respective signals, but the proposed combination of the methods allows to suppress the maternal ECG and when the level of noise is low it leads to an effective separation of the twins' signals

An approach to unsupervised classification

Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method

Monitoring of fetal movements based on actogram signal analysis

At present, biophysical fetal monitoring relies mainly on evaluation of a fetal heart rate (FHR). Absence of the FHR variability indicates central nervous system depression associated with hypoxia. The analysis of fetal heart rate segments identified with the aid of information on fetal movement activity provides much better results than analysis of the whole signal. Automatic recording of the fetal movement activity in a form of actogram signal provided by new models of fetal monitors becomes very common. For evaluation of information content of the actogram signal, the measurement instrumentation has been developed. The examined group comprised 20 patients and the total time of recording was 10 hours. Correlation between movements observed by clinical experts on ultrasonographic image and actogram trace recorded by fetal monitor was analysed. Although for head, arms, legs and trunk movements just visual analysis let observe their correlation with actogram signal, but in case of breathing movement no correlation was noted. Depending on movement type the detection efficiency was in range from 54 % to 80 %

Computerized fetal monitoring based on bioelectric signals from maternal abdomen

The paper presents an instrumentation set for effective diagnostics of early symptoms of fetal distress in high-risk pregnancy based on bioelectric signals recording. The representative database of records has been established that enabled the complex analysis of signals recorded on a surface of maternal abdomen. As an outcome, the effective algorithms have been developed for processing both the fetal electrocardiogram and the signal of electrical activity of a uterine muscle. This allowed the development of unique diagnostic system based on external biosignal measurement module connected to the standard personal computer. In this system, like in a classical fetal monitor, the fetal heart rate (FHR) signal together with contraction activity signal of uterine muscle and fetal movement become an essential source of information on fetal condition. In addition, there is a possibility for spectral analysis of FHR signal as well as a morphology assessment of fetal ECG signal

The prognostic value of electrohysterography in prediction of premature labour

Premature birth is the leading cause of a neonatal death, so, it is extremely important to distinguish the pregnancy at risk of preterm threatening labour. The electrohysterography seems very promising as a method which enables noninvasive recording of readable bioelectrical signal of uterine muscle. The developed instrumentation enabled simultaneous recording of bioelectrical signals by means of electrodes attached to abdominal wall and mechanical activity of uterine muscle using fetal monitor. Material comprised 27 patients in physiological pregnancy (27 ÷ 40 week), and 21 patients (23 ÷ 36 week) with the symptoms of threatening premature labour. The obtained results showed that quantitative parameters of detected uterine contractions: amplitude and contraction area, obtained both for mechanical and electrical activity, were statistically significant (p < 0.05) to distinguish the patients at risk of premature labour. However, their reliability is low because they strongly depend on individual patient features. We consider the parameters characteristic for electrohysterogram exclusively e.g. contraction power and its median frequency as more useful (p < 0.05). Noninvasive electrohysterography ensures higher sensitivity and specificity for recognition of uterine contraction activity in comparison to classical mechanical method

Reconstruction of FHR series recorded via ultrasound - method validation using abdominal fetal electrocardiography

Analysis of variability of the fetal heart rate (FHR) is very important for fetal wellbeing assessment. 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. Nowadays, monitoring instrumentation is based on Doppler ultrasound technology. The fetal monitors provide the output signal in a form of evenly spaced measurements. The goal of this work is to present a new method for the FHR signal processing, which enables extraction of time series of consecutive heartbeat intervals from the evenly repeated values. The proposed correction algorithm enables recognition and removal of the duplicated measurements. Reliable evaluation of the algorithm requires the reference event series, thus the FHR signals were obtained from abdominal fetal electrocardiograms to be used in this research study