Перегляд за автором "Posokhov, M. F."

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  • Зображення мініатюри
    Публікація
    Assessment of Electrocardiogram Quality Using Lossless Compression Technique for Heart Rate Variability Analysis
    (IEEE, 2019) Кривенко, С. А.; Pulavskyi, A. A.; Kolesnikova, O. V.; Lukin, V. V.; Posokhov, M. F.; Krivenko, S. S.
    A simple method of assessment of electrocardiogram (ECG) quality based on lossless data compression is proposed. It is intended for a quick preliminary assessment of ECG in terms of further analysis of heart rate variability (HRV) based on a sequence of R-peaks. The method does not require a priori information about the type, nature and intensity of noise. The only output parameter of pre-processing step is the ECG compression ratio. Thresholds of compression ratio are determined, above which the results of further analysis of HRV are the same as for the corresponding ECG not distorted by noise. For a sampling frequency of 250 Hz, the threshold is equal to 1.4, for a frequency of 2000 Hz, it is 4.0. The results are shown for both synthetic ECG and ECG received using a personal screening diagnostic device.
  • Зображення мініатюри
    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.
  • Зображення мініатюри
    Публікація
    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).