Breath detection using a fuzzy neural network and sensor fusion

We have developed and trained a fuzzy neural network (FNN) to detect individual breaths using information from multiple independent noninvasive ventilation sensors. We derive input features from simultaneous recordings from impedance and inductance plethysmographs, and a pneumotachometer while healthy adults performed several different combinations of ventilation and motion. We first tested our FNN using membership functions, rules and consequent sets derived using a heuristic approach. Using all features, on 4 subjects we found that the average rate of combined false-positive and false-negative detections was 5.1%. When we trained our FNN using a gradient descent algorithm, the average rate of combined false-positive and false-negative detections was reduced to 2.6%.