Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

Jhon Cárdenas; Alvaro D. Orjuela-Cañón; Alexander Cerquera; Antonio Ravelo

doi:10.1117/12.2284957

17 November 2017 Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

Jhon Cárdenas, Alvaro D. Orjuela-Cañón, Alexander Cerquera, Antonio Ravelo

Author Affiliations +

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057219 (2017) https://doi.org/10.1117/12.2284957
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

Abstract

Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

Citation Download Citation

Jhon Cárdenas, Alvaro D. Orjuela-Cañón, Alexander Cerquera, and Antonio Ravelo "Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057219 (17 November 2017); https://doi.org/10.1117/12.2284957

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