Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring data

Aim. To improve diagnostics of arterial hypertension (AH) at early stages. Material and methods. In 34 relatively healthy individuals and 72 AH patients, 24-hour blood pressure monitoring (BPM) was performed. In all hypertensive patients, no target organ damage was registered, office BP levels were...

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Main Authors: V. G. Vilkov, R. G. Oganov, S. A. Shalnova
Format: Article
Language:Russian
Published: «SILICEA-POLIGRAF» LLC 2006-06-01
Series:Кардиоваскулярная терапия и профилактика
Subjects:
Online Access:https://cardiovascular.elpub.ru/jour/article/view/1194
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author V. G. Vilkov
R. G. Oganov
S. A. Shalnova
author_facet V. G. Vilkov
R. G. Oganov
S. A. Shalnova
author_sort V. G. Vilkov
collection DOAJ
description Aim. To improve diagnostics of arterial hypertension (AH) at early stages. Material and methods. In 34 relatively healthy individuals and 72 AH patients, 24-hour blood pressure monitoring (BPM) was performed. In all hypertensive patients, no target organ damage was registered, office BP levels were below 159/94 mm Hg. Results. Using the program imitating artificial neuronal nets, a model was developed that correctly diagnosed early AH stages, by 24-hour BPM data, in people without evident BP increase. Conclusion. Non-linear multiple modeling substantially improved early AH diagnostics in people without evident BP increase at rest.
format Article
id doaj-art-9cc7399876cc4f57a6223e1c12dc7b5a
institution Kabale University
issn 1728-8800
2619-0125
language Russian
publishDate 2006-06-01
publisher «SILICEA-POLIGRAF» LLC
record_format Article
series Кардиоваскулярная терапия и профилактика
spelling doaj-art-9cc7399876cc4f57a6223e1c12dc7b5a2025-08-20T03:43:30Zrus«SILICEA-POLIGRAF» LLCКардиоваскулярная терапия и профилактика1728-88002619-01252006-06-01532831906Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring dataV. G. Vilkov0R. G. Oganov1S. A. Shalnova2State Research Center for Preventive Medicine, Federal Agency for Health and Social Development. MoscowState Research Center for Preventive Medicine, Federal Agency for Health and Social Development. MoscowState Research Center for Preventive Medicine, Federal Agency for Health and Social Development. MoscowAim. To improve diagnostics of arterial hypertension (AH) at early stages. Material and methods. In 34 relatively healthy individuals and 72 AH patients, 24-hour blood pressure monitoring (BPM) was performed. In all hypertensive patients, no target organ damage was registered, office BP levels were below 159/94 mm Hg. Results. Using the program imitating artificial neuronal nets, a model was developed that correctly diagnosed early AH stages, by 24-hour BPM data, in people without evident BP increase. Conclusion. Non-linear multiple modeling substantially improved early AH diagnostics in people without evident BP increase at rest.https://cardiovascular.elpub.ru/jour/article/view/1194blood pressurearterial hypertensionearly diagnosticsmodeling
spellingShingle V. G. Vilkov
R. G. Oganov
S. A. Shalnova
Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring data
Кардиоваскулярная терапия и профилактика
blood pressure
arterial hypertension
early diagnostics
modeling
title Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring data
title_full Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring data
title_fullStr Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring data
title_full_unstemmed Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring data
title_short Neuro-net model for early arterial hypertension diagnostics using 24-hour blood pressure monitoring data
title_sort neuro net model for early arterial hypertension diagnostics using 24 hour blood pressure monitoring data
topic blood pressure
arterial hypertension
early diagnostics
modeling
url https://cardiovascular.elpub.ru/jour/article/view/1194
work_keys_str_mv AT vgvilkov neuronetmodelforearlyarterialhypertensiondiagnosticsusing24hourbloodpressuremonitoringdata
AT rgoganov neuronetmodelforearlyarterialhypertensiondiagnosticsusing24hourbloodpressuremonitoringdata
AT sashalnova neuronetmodelforearlyarterialhypertensiondiagnosticsusing24hourbloodpressuremonitoringdata