Detection of Hypoxia in Humans

Abstract

Humans exposed to high altitude or high acceleration conditions for prolonged periods of time often exhibit hypoxic symptoms. The commencement of physiological and cognitive changes due to the onset of hypoxia may not be immediately apparent to the exposed individual. These changes can go unrecognized for minutes and even hours; and may lead to serious performance degradation or complete incapacitation. Despite interest in the detection of hypoxia, existing detection methodologies have limited scope of reliable performance in the field. This research investigates the underlying physiological and cognitive signals under environments prone to development of hypoxic symptoms. Attention is focused toward developing temporal models for physiological signals, state-augmented Kalman filters, and designing of hypoxia detection frameworks. These frameworks utilize parallel fusion architectures that use Bayesian decision criteria to combine local decisions from ensembles of raw physiological, cognitive, and environmental signals, along with outputs of local classifier/detectors trained using machine learning techniques, to provide reliable hypoxia detection.Ph.D., Electrical Engineering -- Drexel University, 201