Automated (AI) Analysis of Sleep Disordered Breathing

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DESCRIPTION (provided by applicant) This training grant is designed to advance the academic career of Dr. Indu Ayappa by building on the applicant?s strong biomedical engineering and computer background and providing comprehensive multi-disciplinary training which will allow her to become an independent investigator. The applicant?s career goals are to enter full time academic research in sleep physiology. Training activities proposed will include academic course work designed to support the research program and expose her to aspects of clinical research in sleep, neural science and artificial intelligence techniques. She will be mentored by David Rapoport, M.D., Joyce Walsleben, Ph.D., and Maurice Ohayon, M.D., Ph.D., as well as faculty in computer science and neurophysiology. The aims of the planned research are to develop an artificial intelligence system for the identification and quantification of sleep disordered breathing (SDB) based solely on non-invasive cardiopulmonary signals collected during routine polysomnography. This will simplify, standardize and improve the diagnosis of SDB and facilitate research in this area. With the long term goal of physiologic characterization of the spectrum of SDB in order to clinically diagnose upper airway resistance syndrome (UARS), the aims of this project are to: 1. Extract features from the nasal cannula airflow signal to characterize the state of resistance/collapsibility of individual breaths. These include amplitude, inspiratory flow contour, Ti/Ttot and presence of vibration which 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 (pressure/flow). 2A. Incorporate information from this classification of individual breaths to detect and classify respiratory events based on the flow signal alone using a trained neural network. 2B. Evaluate the utility of including additional cardiopulmonary signals like oxygen saturation, heart rate, pulse transit time and rib/abdominal movements (amplitude and phase) in the detection and classification of these events. Successful completion of the training and research program will allow Dr. Ayappa to contribute independently to research in the field of sleep physiology.