Breath detection using fuzzy sets and sensor fusion

We developed a breath detection algorithm which uses fuzzy sets to classify signals from multiple noninvasive sensing technologies. We tested our algorithm using simultaneous recordings from impedance and inductance plethysmographs, while healthy adults performed several different combinations of ventilation and motion. For 4 subjects, the average rates of false positive and false negative detection were 0.6% and 2.2%, respectively.