Source: Annual Congress 2001 - Monitoring acute lung disease
Disease area: Respiratory critical care

Abstract

The experiments were performed on 12 anesthetized, tracheostomized, and spontaneously breathing adult cats before and 1 hour after bilateral phrenicotomy. Three parameters: frequency of breathing f, tidal volume VT and minute ventilation VE were measured during resting ventilation. Measurements of features were repeated 9 times for each the animal in the two states of the respiratory system. The data set contained two subsets by 108 of 3-dimensional measurement vectors. The question was if it possible to recognize on the basis of the collected data whether the cats breathed with or without the diaphragm. To study this issue the methods of statistical pattern recognition theory were applied. With these methods decision rules were constructed that allowed evaluating the considered parameters in their mutual context. Four different types of the decision rules were used and compared: the standard k-nearest neighbor rule (k-NN), fuzzy version of k-NN rule, standard k-NN rule with a preclassifier and the fuzzy k-NN rule with the preclassifier. For each of these decision rules all parameter combinations were analyzed. The highest probability of correct classification, equal 98%, was offered by the fuzzy k-NN rule with the preclassifier and the 2 parameters f and VT. This high probability indicates the strong influence of diaphragm denervation on the ventilation parameters f and VT. The parameter VE was redundant and its use decreased the probability of correct classification by 1%, i.e. to the value of 97%. The results show that presented approach based on statistical pattern recognition methods is an effective tool for the medical decision making.

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B. Sokolowska, K. Budzinska, A. Jozwik (Warsaw, Poland). Recognition of the respiratory pathology by statistic methods. Eur Respir J 2001; 16: Suppl. 31, 1650

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