Real-Time Hypoxia Prediction Using Decision Fusion

Abstract

Humans who operate in high altitudes for prolonged durations often suffer from hypoxia. 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. A dynamic system capable of monitoring and detecting decreased physiologic states due to the onset of hypoxia has the potential to prevent adverse outcomes. In this study, we develop a real-time hypoxia monitoring system based on a parallel M -ary decision fusion architecture. Blood oxygen saturation levels and altitude readings are the inputs and estimates of the level of hypoxia are the outputs. We develop new temporal evolution models for blood oxygen saturation and functional impairment with respect to varying altitude. The proposed models enable accurate tracking of various hypoxia levels based on the duration of stay of the subject at an altitude. Using a Bayesian decision-making formulation, the system generates global estimates of the degree of hypoxia. The detection system is tested against synthetic and real datasets to demonstrate applicability and accuracy.

Decision fusion in healthcare and medicine: a narrative review

“…Intelligent methods such as DF are widely used in healthcare because of capability of this technique to handle complex data within the healthcare environment. Some of the advantages of this technique include good management of non-identical data sources (32), reduce dimensionality, decrease noise (33), improve model performance (34,35) and enhance the interpretation of the results (36). Certain challenges include low quality of data used for analysis, complexity of the data (40,41), selecting the appropriate methods depending on the type of data and computational burden, which should be considered in future studies for achieving better results.…”

“…An example of this method is the Bayesian theory, which has been effectively used in fusion event probabilities and managing randomness in the DF technique. The Causal independence model (36), the Belief function theory method (45), the discrete Bayes classifier method (46), the Bayesian method (34,(47)(48)(49)(50)(51) and the likelihood method (52) are all examples of probabilistic methods that have been used in various studies. The evidential method pertains to combining pieces of evidence to calculate the probability of an event.…”

“…The key advantages of the DF technique include: effective management of non-homogeneous data sources (32), efficient combination of data sources, reduction in dimensionality and certain noises (33), improved model performance (34,35), ease of interpretation of results (36), clear display and summarization of information, ease of information extraction, improved decision-making and cost reductions. Other potential advantages include: decrease in the loss of information and meaningful use of information, which improves results, data quality, reliability and entity detection (37).…”

The Paradox of Happy Hypoxia in COVID 19

“… Another reason could be clotting mechanisms and micro emboli like in cases of pulmonary embolism. It causes an increase in alveolar dead space and alveolar hypoventilation [10] if the minute volume remains unchanged. Impaired alveolar diffusion of oxygen will lead to 'silent hypoxia' and a consistent drop in oxygen saturation.…”

Relating Aircraft Altitude with Pilot’s Physiological Variables: Towards Increasing Safety in Light-sport Aviation

“…The onset of hypoxia is identified based on the changes in their cross-correlation signals. Acharya et al (2016) present a M-ary decision fuzzy architecture capable of classifying the degree of induced hypoxia as a function of the duration of exposure to different altitude profiles. The proposed monitoring system takes blood oxygen saturation levels and altitude readings as inputs and estimates of the level of hypoxia as outputs.…”