Development of a decision support system for neuro-electrostimulation: Diagnosing disorders of the cardiovascular system and evaluation of the treatment efficiency

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@Article{KUBLANOV:2019:ASC,

• author = "Vladimir Kublanov and Anton Dolganov",
• title = "Development of a decision support system for neuro-electrostimulation: Diagnosing disorders of the cardiovascular system and evaluation of the treatment efficiency",
• journal = "Applied Soft Computing",
• volume = "77",
• pages = "329--343",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Decision support, Machine learning, Feature selection, Arterial hypertension, Heart rate variability",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2019.01.032",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494619300377",
• abstract = "The study describes a preliminary stage of the decision support system development for physician performing neuro-electrostimulation of neck neural formations for patients suffering from cardiovascular system disorders. The arterial hypertension was used as the clinical model of the disorders. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recorded while performing tilt-test functional load. Heart rate variability signal is an indirect mean of accessing autonomic nervous system functioning. Disturbances of the autonomic nervous system are essential in pathology of arterial hypertension. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate treatment efficiency. Predictions based on the selected heart rate variability features have a high level of agreement with the arterial pressure dynamics. The results indicate the potential of the proposed decision support system",
• keywords = "genetic algorithms, genetic programming, Decision support, Machine learning, Feature selection, Arterial hypertension, Heart rate variability",

}

Genetic Programming entries for Vladimir Kublanov Anton Dolganov

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