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Telecommunications and Radio Engineering

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ISSN Druckformat: 0040-2508

ISSN Online: 1943-6009

SJR: 0.185 SNIP: 0.268 CiteScore™:: 1.5 H-Index: 22

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IMPROVING THE RELIABILITY OF RHINOMANOMETRY DIAGNOSTICS BY CONSIDERING STATISTICAL CHARACTERISTICS OF MEASURED SIGNALS

Volumen 73, Ausgabe 7, 2014, pp. 647-655
DOI: 10.1615/TelecomRadEng.v73.i7.70
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ABSTRAKT

Current paper describes the technique of automated analysis based on the rhinomanometric diagnostics, as well as the possible ways to improve the reliability of measuring technique by optimization of algorithmic model to express-monitoring of nasal breathing. Our results have shown the applicability of suggested techniques − variance and piecewise regression transformations of dynamic signals − to obtain additional informative parameters under the time limit biological observation. It has been practically revealed that space dimension of informative parameters affects on the error probability of diagnostics. The value of proposed technique is that it permits to study the influence of individual characteristics of the patient's breathing on rhinomanometry results, and to improve automated diagnosis during respiratory testing.

REFERENZEN
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REFERENZIERT VON
  1. Tymkovych Maksym, Avrunin Oleg, Gryshkov Oleksandr, Semenets Valerii, Glasmacher Birgit, Ice Crystals Microscopic Images Segmentation Based on Active Contours, 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), 2019. Crossref

  2. Avrunin Oleg G., Nosova Yana V., Abdelhamid Ibrahim Younouss, Pavlov Sergii V., Shushliapina Natalia O., Bouhlal Natalia A., Ormanbekova Ainur, Iskakova Aigul, Harasim Damian, Research Active Posterior Rhinomanometry Tomography Method for Nasal Breathing Determining Violations, Sensors, 21, 24, 2021. Crossref

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