Software for Statistical Processing and Modeling of a Set of Synchronously Registered Cardio Signals of Different Physical Nature

Lupenko, Serhii; Lytvynenko, Iaroslav; Sverstiuk, Andrii; Shelestovskyi, Borys; Horkunenko, Andrii

doi:10.32782/cmis/2864-17

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Such a vector of cyclic rhythmically connected random processes is called a vector of cyclic rhythmically connected random processes with an irregular (variable) rhythm. The vector of cyclic rhythmically connected random processes and cyclic random process are widely used as mathematical models of the synchronously registered cardio signals that differ in physical nature [ 49 ], the processes of multiple cracking of nanocoating [ 50 ], and the processes of ordering surface structures of materials [ 51 ]. These mathematical models and methods of signal processing have been represented in the form of a taxonomic tree [ 52 ] and an ontology [ 53 ] in onto-oriented expert support systems for model decision making.…”

Section: The New Mathematical Model Of Eeg Signals In Bci Systems In ...mentioning

confidence: 99%

Advanced Modeling and Signal Processing Methods in Brain–Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes

Lupenko

Butsiy

Shakhovska

2023

Sensors

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In this study, we substantiate the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain–computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain–computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain–computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain–computer interface operator is experimentally established. The application of Bessel’s inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain–computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain–computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.

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Section: The New Mathematical Model Of Eeg Signals In Bci Systems In ...mentioning

confidence: 99%

Advanced Modeling and Signal Processing Methods in Brain–Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes

Lupenko

Butsiy

Shakhovska

2023

Sensors

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“…The first step is to define the parameters of the signal [13]. This includes setting the sampling frequency, duration of the signal, and fetal heart rate [14].…”

Section: Generation Of Fetal Ecg Signalmentioning

confidence: 99%

Vector of Diagnostic Features in the Form of Decomposition Coefficients of Statistical Estimates Using a Cyclic Random Process Model of Cardiosignal

Martsenyuk

Сверстюк

Kłos-Witkowska

et al. 2019

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applicat

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This article discusses the importance of timely and accurate detection of fetal distress during pregnancy to reduce the risk of adverse outcomes. One non-invasive method of fetal monitoring is through the use of fetal cardiodiagnostic systems, which record the fetal ECG signal. However, selecting a reliable fetal ECG signal can be challenging, as the signal may be weak and prone to interference. The article presents several existing methods for extracting useful fetal ECG signals from a mixture, including blind signal separation, adaptive filtering, synphase method, component method, spectral method, and bispectral processing. However, the limitations of these methods make it necessary to develop a new method that can consider time-frequency characteristics and phase-time parameters of the ECG signal simultaneously. The article proposes a new algorithm and method for extracting fetal ECG signals, which involves several steps, including registration of maternal ECG signals, synphase detection of fetal ECG signals and adaptive filtering. The proposed method was tested on a generated ECG signal and was found to be effective in extracting the fetal ECG signal from noisy and artifactcontaining signals. The method and algorithm for detecting the fetal ECG signal in the presence of obstacles is implemented in the MATLAB environment.

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Computer modeling of cardiac rhythm based on vector of stationary random sequences

Lupenko,

Lytvynenko,

Onyskiv

et al. 2022

Visnyk TNTU

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The article is devoted to a computer modeling method of electrocardiogram rhythm based on a mathematically justified model in the form of a vector of stationary random sequences. The developed computer modeling method allows for generating realizations of vector electrocardiogram rhythm signal (vector components of stationary random sequences) for different types of electrocardiogram signals, both normal and with various types of rhythm pathologies. The modeling of electrocardiogram rhythms was carried out based on the obtained statistical information in the form of estimates of the mathematical expectation and variance of the components of the vector of stationary random sequences. It has been shown that the obtained estimates of statistical characteristics of the modeled vector components (components that describe the electrocardiogram rhythm) are within confidence intervals, which is an indication of the correctness of the experiments conducted using the developed computer simulation method. The accuracy of the computer simulation method for generating realizations of the vector components of stationary random sequences has been investigated, and the error of the computer simulation does not exceed 13% for the investigated vector components.

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Software for Statistical Processing and Modeling of a Set of Synchronously Registered Cardio Signals of Different Physical Nature

Cited by 4 publications

References 9 publications

Advanced Modeling and Signal Processing Methods in Brain–Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes

Advanced Modeling and Signal Processing Methods in Brain–Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes

Vector of Diagnostic Features in the Form of Decomposition Coefficients of Statistical Estimates Using a Cyclic Random Process Model of Cardiosignal

Computer modeling of cardiac rhythm based on vector of stationary random sequences

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