Presentation of the Indicative Factors of Heart Rate Variability for Hypertension Swift-diagnostics

• Published in: 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) pp. 0428 - 0431
• Main Authors: Dolganov, Anton, Kublanov, Vladimir
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: arterial hypertension; Data visualization; Decision support systems; diagnostics; Encoding; Genetic programming; Heart rate variability; Hypertension; Machine learning; sparse coding
• DOI: 10.1109/SIBIRCON48586.2019.8958298

The paper describes a visualization methodology of the indicative factors of the short-term heart rate variability for the arterial hypertension express-diagnostics. Biomedical signals were recorded in the course of the functional studies, which included rest state, tilt-test state and aftereffect state. Each state of the functional study was 5 minutes long. Factors complexes were obtained in earlier studies by means of the genetic programming application and quadratic discriminant analysis machine learning technique. In the article proposed alternative way to evaluate decision functions of discriminant analysis, which does not involve matrix multiplication. The proposed visualization is presented for different subjects: for volunteers with normal pressure and for patients, diagnosed with the arterial hypertension. It was shown, that for different subject's different factors are 'activated' giving an input to the classification decision. The proposed methodology allowed to conclude that diagnostically indicative factors complexes are able to use personalized data of a patient in diagnostics.