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New techniques for automatic invasive and noninvasive identification of inspiratory flow limitation (IFL) are presented. Data were collected from 11 patients with full nocturnal polysomnography and gold-standard esophageal pressure (Pes) measurement. A total of 38,782 breaths were extracted and automatically analyzed. An exponential model is proposed to reproduce the relationship between Pes and airflow of an inspiration and achieve an objective assessment of changes in upper airway obstruction. The characterization performance of the model is appraised with three evaluation parameters: mean-squared error when estimating resistance at peak pressure, coefficient of determination, and assessment of IFL episodes. The model's results are compared to the two best-performing models in the literature. The obtained gold-standard IFL annotations were then employed to train, test, and validate a new noninvasive automatic IFL classification system. Discriminant analysis, support vector machines, and Adaboost algorithms were employed to objectively classify breaths noninvasively with features extracted from the time and frequency domains of the breathspsila flow patterns. The results indicated that the exponential model characterizes IFL and subtle relative changes in upper airway obstruction with the highest accuracy and objectivity. The new noninvasive automatic classification system also succeeded in identifying IFL episodes, achieving a sensitivity of 0.87 and a specificity of 0.85.