Study of Phylogenetic for Computational Analysis of Sleep Apnea Syndrome for Patient (Healthcare & Treatment) Using Machine Learning (Robot Vision)

EasyChair Preprint no. 6202

Abstract

The reconstruction of the Tree of Life is a classical and Complex problem in evolutionary biology that has benefited from numerous branches of mathematics, including probability, information theory, Proof Theory, combinatorics, and geometry. Additionally, advances in computer technology Design and architecture such as parallel, Centralized and distributed computing, and programs that exploit them efficiently in combination with the continual development of faster search strategies promise to make even larger phylogenetic problems increasingly tractable. However, the NP-completeness of the phylogeny problem represents a fundamental limitation in efforts to unearth the tree of life. Modern DNA sequencing technologies are producing a deluge of new genetic data, transforming how we view the Tree of Life and how it is reconstructed.Sleep Quality Analysis is currently accessed through different means, from simple commercial devices that measure activity through an accelerometer or more complex ones that use oxymetry, to medical devices such as [1]polysomnography(PSG) that can be used indirectly to estimate sleep quality. which is an expensive procedure involving much effort for the patient. Multiple systems have been proposed to address this situation, including performing the examination and analysis in the patient’s home, using sensors to detect physiological signals that are automatically analyzed by algorithms. However, the precision of these devices is usually not enough to provide clinical diagnosis. Therefore, the objective of this review is to analyze already existing algorithms that have not been implemented on hardware but have had their performance verified by at least one experiment that aims to detect obstructive sleep apnea to predict trends.

Keyphrases: Algorithms, Algorithms review sleep apnea, combinatorics, Databases, evolutionary biology, medical, phylogenetic, proof theory

@Booklet{EasyChair:6202,
  author = {Romil Rawat and Vinod Mahor and Shrikant Telang},
  title = {Study of Phylogenetic for Computational Analysis of Sleep Apnea Syndrome for Patient (Healthcare & Treatment) Using Machine Learning (Robot Vision)},
  howpublished = {EasyChair Preprint no. 6202},

  year = {EasyChair, 2021}}