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Prediction of inverse relationship between compression phase duration and expulsive airflow during voluntary cough in humans by a joint neural network biomechanical computational model
Author(s) -
Bolser Donald C,
Pitts Teresa E,
O'Connor Russell,
Segers Lauren S,
Sapienza Christine M,
Davenport Paul W,
Morris Kendall F,
Lindsey Bruce G
Publication year - 2013
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.27.1_supplement.930.16
Subject(s) - compression (physics) , airflow , airway , medicine , physical medicine and rehabilitation , simulation , computer science , anesthesia , materials science , engineering , mechanical engineering , composite material
Previous computational modeling efforts of the cough motor control system have focused on neural networks and predicting motor output patterns. We speculated that a recent joint neuromechanical model of the cough central motor control system that incorporated airway and chest wall mechanics would be useful in predicting mechanical aspects of cough in humans. Simulations of cough with this model produced large airflows and pressures with mechanical and temporal features very similar to those recorded during cough in humans. In particular, compression phase durations and airflows during simulated coughing varied by approximately 60%. Compression phase durations were longer during simulated coughs with low airflows. In human studies, healthy subjects performed a magnitude production protocol by producing weak, moderate, or strong voluntary coughs. Peak expiratory airflows increased by approximately 40% and abdominal electromyogram amplitudes approximately doubled from weak to strong voluntary coughs. Compression phase durations shortened significantly from weak to strong coughs (weak‐351±33 ms, moderate‐253±86 ms, strong‐217±39 ms, p<0.05). The simulation results represent an emergent property and support the predictive value of a joint neuromechanical computational model for human airway protective behaviors. Supported by NIH HL103415, HL89104, HL89071, NS19814.