Перегляд за Автор "Ivanushkina, N. G."

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    A Computational Model of Electrophysiological Properties of Cardiomyocytes
    (КПІ ім. Ігоря Сікорського, 2018) Ivanushkina, N. G.; Ivan’ko, E. O.; Prokopenko, Yu. V.; Redaelli, A.; Tymofieiev, V. I.; Visone, R.; Іванушкіна, Н. Г.; Іванько, К. О.; Прокопенко, Ю. В.; Редаеллі, А.; Тимофєєв, В. І.; Вісоне, Р.; Иванушкина, Н. Г.; Иванько, Е. О.; Прокопенко, Ю. В.; Редаэлли, А.; Тимофеев, В. И.; Висонэ, Р.
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    Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques
    (КПІ ім. Ігоря Сікорського, 2024) Bondarev, V. R.; Ivanko, K. O.; Ivanushkina, N. G.
    The study is dedicated to the problem of classification of structural and functional development stage of cardiomyocytes derived from the induced pluripotent stem cells with application of the digital image processing methods and machine learning algorithms, in particular, neural networks. Cell regenerative therapy has become one of the most promising treatment options for patients with heart failure. But since cardiomyocytes are objects with a high level of complexity and have significant morphological variability, automatic classification is complicated by the lack of implemented methods. That's why researches in this area are a major global public health priority. The initial data set used in this study is a publicly open set of confocal microscopic images of cardiomyocytes which can be divided into five classes based on the morphological features (the structure of the transverse T-tubule). A small amount of input data leads to the need of using augmentation methods. Methods that prevent the alteration of the transverse T-tubule, which is an important parameter for correct classification of the development of cardiomyocytes, are used. Histogram equalization technique is used to enhance the contrast and dynamic range of the confocal microscopic images with the method of contrast-limited adaptive equalization. This helped to improve the local contrast of the analyzed images and highlight the main elements of the cardiomyocyte. Finally, Chan–Vese method, which belongs to the regional segmentation methods, is chosen for the image segmentation and removing artifacts and/or parts of other cells from the image. A pre-processed and augmented dataset is used for training of the convolutional neural network having an architecture with hierarchical structure and residual block usage. The model is evaluated based on the confusion matrix and the heat maps of different convolutional layers are analyzed. Images from the classes with a large number of mutual errors are also considered. Based on the analysis, several classes of structural and functional development of cardiomyocytes are combined. Final accuracy of the model for defining the cardiomyocytes maturation stage achieved 77%.
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    Identification and Assessment of Electrocardiographic Markers of Cardiac Electrical Instability
    (КПІ ім. Ігоря Сікорського, 2017) Ivanko, K. O.; Ivanushkina, N. G.; Karplyuk, Y. S.; Іванько, Катерина Олегівна; Іванушкіна, Наталія Георгіївна; Карплюк, Євгеній Сергійович; Иванько, Екатерина Олеговна; Иванушкина, Наталья Георгиевна; Карплюк, Евгений Сергеевич
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    Myocardial Ischemia Detection Using a Reduced Number of ECG Leads
    (КПІ ім. Ігоря Сікорського, 2022) Mnevets, A. V.; Ivanushkina, N. G.; Ivanko, K. O.
    The study is devoted to the investigation of the electrocardiographic (ECG) features to distinguish norm and myocardial ischemia in reduced set of electrocardiographic leads. In particular, for myocardial ischemia detection the spectral features of the electrocardiographic signal and characteristics of the shape of ECG waves are considered. The main features commonly used for myocardial ischemia detection are described in the paper, as well as more reliable analogs are proposed for the considered task. The approach for ECG signal preprocessing, identification of the necessary signal segments and subsequent calculation of features is described in detail. The considered features are based on the areas under the characteristic waves of the ECG signal and the spectral distribution of these waves. The most informative features for myocardial ischemia detection are identified and selected from the initial set of parameters which led to a two-fold reduction in number of ECG leads comparing to the standard 12-lead electrocardiogram. The techniques for determining the proposed features, namely the ratio of the area under T wave to the area under the P wave, as well as the ratio of the area under T wave to the area of the entire cardiac cycle, are considered. These features together with other calculated parameters are assumed to describe the majority of pathology cases and gave a high accuracy of the classification ECG to norm and ischemic myocardial diseasesince they reflect the bioelectrical processes that occur in the presence of myocardial ischemia and manifest themselves on the surface ECG. Based on the analysis of principal components and the method t-distributed stochastic neighbor embedding, the distribution of data in the space of features that characterize the classes of norm and pathology was shown. Raw ECG data in norm and with cases of myocardial ischemia were obtained from the ”PTB Diagnostic ECG Database” used in ”The PhysioNet/Computing in Cardiology Challenge 2020”. This database contains 22353 ECG records from 290 persons with 12 ECG leads (I, II, III, aVR, aVL, aVF, and V1–V6). The database contains the high-resolution ECG signals, which enabled to obtain 10,000 cardio cycles presenting norm and myocardial ischemia pathology for the subsequent training the machine learning algorithms. Based on the obtained features, various machine learning algorithms were trained and the accuracy was compared on different combinations of ECG leads. Аs a result of cross-validation, the accuracy of myocardial ischemia detection was 99% with a standard deviation of 0.4% for 6 leads (I, II, III, AVR, AVL, AVF) and 93% with a standard deviation of 0.12% for one lead (I). Thus, it was shown, that with machine learning methods it is possible to recognize ischemic myocardial disease with high accuracy and stability using six standard ECG leads or only one ECG lead.
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    Neural Networks Detection of Low-Amplitude Components on ECG Using Modified Wavelet Transform
    (КПІ ім. Ігоря Сікорського, 2024) Mnevets, A. V.; Ivanushkina, N. G.
    This study is devoted to identification of low amplitude components from ECG signals by different timefrequency analysis methods when main power spectrum falls on high-amplitude components. It was also analyzed the problem of choosing correct scale system for determination low-amplitude components on the scalogram by artificial intelligence models. As a result of the study, several modifications of the continuous wavelet transform were proposed. First modification was based on the use of a scaling function and a modified wavelet. Second modification was based on the use of cosine similarity at each iteration of convolution followed by the use of a scaling function. The main idea of the study was to modify the wavelet transform in such a way as to select the components which has the target amplitude and reduce all other components that complicate the neural networks analysis of the interested fragments of the signal. Also, possible procedures for signal restoring were proposed for preserving the effect of using scaling modifications. The testing of the proposed modified algorithms was carried out on the basis of artificially created signals as well as on the basis of real ECG signals with late potentials superimposed on them. Visual analysis of scalograms and signal reconstructions obtained using the modified wavelet transform showed that the modified wavelet transform is capable of extracting low-amplitude components from the signal with much greater spectral power than the transform without modifications. In addition, the ability of common neural network models to distinguish between cardiac cycles with and without late potentials was tested. As a result, it was found that models that were trained on scalograms obtained using a modified wavelet transform train faster and are less susceptible to local minima stucking. The results of classification of signals with and without late potentials based on trained neural network models showed that training using scalograms obtained on the base of a modified wavelet transform allows achieving 99% classification accuracy, which is 1-49% more than that using scalograms obtained on the base of on the classical wavelet transform.
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    Solving the Inverse Problem of Relationship Between Action Potentials and Field Potentials in Cardiac Cells
    (КПІ ім. Ігоря Сікорського, 2021) Ivanushkina, N. G.; Ivanko, K. O.; Shpotak, M. O.; Prokopenko, Y. V.
    Multiple electrode array (MEA) systems are the instrument platforms being used for cardiac extracellular electrophysiology investigation. Key applications of MEA technology are disease modeling and screening of drug effects. To solve these problems the efforts of many scientists are directed to signal processing and analysis of field potentials (FP) measured with MEA systems. However, it should be noted the complexity of interpretation of MEA information in non-invasive field potentials measurements of cardiac cells compared to invasive action potential (AP) recordings obtained using patch clamp technology. This study is devoted to the mathematical determination of the relationship between the signals of the electrical activity of cardiomyocytes: internal AP and external FP. Derivation of equations for transfer functions between AP and FP is based on field theory. This article provides a solution to the inverse problems of the relationship between AP and FP. Numerical experiments demonstrate the results of the inverse transformation of simulated field potentials signals. To denoise the potentials of the extracellular field of cardiomyocytes, the method combining wavelet transform and processing in eigensubspaces of cardiac cycles is used. The proposed method, based on transfer functions, can be used to determine AP parameters and expand the capabilities of data analysis in MEA systems for diagnosing heart disease and assessing cardiac toxicity during drug development.
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    Комплексный метод для выделения электрокардиосигналов плода из абдоминальных сигналов матери
    (НТУУ "КПИ", 2014) Боровский, И. И.; Иванушкина, Н. Г.; Иванько, Е. О.; Лысенко, Э. Р.; Панасюк, Е. В.; Боровський, І. І.; Іванушкіна, Н. Г.; Іванько, К. О.; Лисенко, Е. Р.; Панасюк, О. В.; Borovskyi, I. I.; Ivanushkina, N. G.; Ivanko, K. O.; Lysenko, E. R.; Panasiuk, O. V.
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    Нейронные сети для распознавания образов поздних потенциалов предсердий
    (НТУУ "КПИ", 2013) Иванушкина, Н. Г.; Иванько, Е. О.; Матвеева, Н. А.; Іванушкіна, Н. Г.; Іванько, Є. О.; Матвеева, Н. О.; Ivanushkina, N. G.; Ivanko, K. O.; Matveeva, N. A.