Перегляд за автором "Kryvenko, L. S."
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Публікація Automatic recognition of congestive heart failure signs in heart rate variability data(IEEE, 2022) Pulavskyi, A. A.; Krivenko, S. S.; Krivenko, S. A.; Linskiy, I. V.; Posokhov, M. F.; Kryvenko, L. S.Automatic screening of the population for congestive heart failure (CHF) is a matter of pressing concern due to the severity of the health consequences resulting in disability and death of people. On the one hand, portable devices working with ECG signals become convenient tools for the lay user due to the simplicity. On the other hand, analyzing the specific behavior of the R-peaks sequence (analysis of heart rate variability) in cardiac pathologies allows identifying the patterns inherent in particular heart dysfunction. Such patterns are effectively differentiated using symbolic dynamics methods and the subsequent application of machine learning methods. In this study, a highly specific model was obtained (sensitivity 0.71, specificity 0.96), suitable for automatic screening of CHF. Its operability and performance characteristics have been verified through testing in several publicly available databases.Публікація Many-To-Many Linear-Feedback Shift Model for Training of Artificial Neural Network in Dentistry(IEEE, 2019) Кривенко, С. А.; Pulavskyi, A. A.; Krivenko, S. S.; Kryvenko, L. S.In this paper, the authors consider how to label and save a large number of images that should be predict in a single file. Technique of automatic labeling the data set with finite element model for training of artificial neural network in tomography are proposed. Simple transparent example of sixteen images for predict in a single HDF5 file training of artificial neural network in tomography show accuracy 100% for training set as well for test set. Then this technique is able to build information model of salivary immune and periodontal status and to evaluate the correlation between salivary immunoglobulin level, inflammation in periodontal tissues and orthodontic pathology. The study was conducted on 139 subjects, which were in the age group of 12-18 years reporting to the Department of Pediatric Dentistry of Kharkiv National Medical University. The atopic group consisted of 103 patients with the following conditions: 76 patients of atopic diseases and gingivitis (Group 1) and 27 patients of atopic diseases, gingivitis and orthodontic pathology (Group 2). Among the 139 subjects, 36 healthy controls formed Group 3. The obtained data prove that there is an immune misbalance in children with atopy and in children with combined atopic and orthodontic pathology. Level of sIgA and IgG is decreased in group of patients with atopy and in group of children with atopic and orthodontic pathology. The information model of salivary immune and periodontal status was built and regression analysis showed that there was strong correlation between inflammation in periodontal tissues and level of immunoglobulins.Публікація Model Discrete Wavelet Transform for Clinical IoT Data and Device Interoperability(IEEE, 2022) Bezruk, V. M.; Krivenko, S. A.; Samochernov, M. B.; Kryvenko, L. S.; Krivenko, S. S.The structure of a Haar Transform dataset has been examined by using pandas for machine learning aims. Statistics have been viewed with pandas and Matplotlib. Correlation between features in a dataset has been improved ten times with Model Discrete Wavelet TransformПублікація The use of lossless compression in the process of post-filtration smoothing of an ECG distorted by high muscle noise(IEEE, 2020) Кривенко, С. А.; Pulavskyi, A. A.; Kryvenko, L. S.; Posokhov, M. F.; Krivenko, S. S.The electrocardiogram (ECG) of the first lead, obtained using portable signal collection tools is noise sensitive. One of the most common and poorly filterable types of noise is muscle artefact. As a result of the simulation, a criterion was found that identifies situations in which the use of kernel-based ECG smoothing after the filtering stage is relevant. This criterion is based on the difference in compression coefficients of the ECG smoothed after filtering and filtered ECG. The use of post-filtering smoothing is recommended if this difference is greater than zero. In this case, post-filtering smoothing improves signal quality in terms of mean-square error. The correctness of the criterion was confirmed on real ECG results (MIT-BIH Arrhythmia Database), distorted by real muscle artefact (MIT-BIH Noise Stress Test Database).