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  4. 9(125)'2014
  5. Evaluation of patient conditions with impaired nasal breathing by fuzzy clustering

Evaluation of patient conditions with impaired nasal breathing by fuzzy clustering

I.G. Perova
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The article describes an approach that allows the procedure to apply fuzzy clustering to discrete time series by converting them to the table view "object-property". Conversion is the replacement of discrete time samples by autoregression model parameters of the second order. Testing of discrete time series of rhinomanometry indicators, such as differential pressure between choana and space under the mask and air flow, which were recorded synchronously with the sampling frequency of 200 Hz was carried out to test the adequacy of the model.
Keywords: fuzzy clustering, autoregression model, rhinomanometry