This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The objective of this project is to implement an artificial intelligence (AI) system for the identification and quantification of the respiratory events during sleep, based on noninvasive cardiopulmonary signals collected during routine polysomnography.
The specific aims of the project are to extract features from the nasal cannula airflow signal that characterize the state of resistance/collapsibility of individual breaths. These will be used as inputs to a neural network that will be trained and evaluated against breaths that have been classified by reference measurement of upper airway resistance. Since clusters of 'abnormal' breaths are generally present during 'respiratory events,' the investigator proposes to incorporate information from this classification of individual breaths to detect and classify respiratory events based on flow signal alone using a trained neural network. Utility of cardiopulmonary signals in the detection and classification of these events will also be evaluated.
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