A fuzzy-classifier system to distinguish respiratory patterns evolving after diaphragm paralysis in the cat.

Abstract

We applied the fuzzy "k-nearest neighbor" (k-NN) classifier of the pattern recognition theory to fathom the abnormal way of breathing resulting from diaphragm paralysis and to distinguish the dominant component, tidal or frequency, of the breathing pattern on which ventilatory compensation relies in such a pathological state. We addressed this issue in the experimental model of diaphragm paralysis as a result of bilateral phrenicotomy in anesthetized, spontaneously breathing cats. Of several variables recorded, we selected two features, minute ventilation and arterial CO(2) tension, that were used for the k-NN analysis. The results demonstrate that the ability to maintain ventilation critically depended on the increase in frequency of breathing. Other breathing pattern strategies were ineffective. The k-NN evaluation with the two selected features discerned the prevailing pattern of breathing with sufficient probability. Such an evaluation may be a useful tool in predicting the development of compensatory strategies in disordered patterns of breathing.