New Approaches to Stratification of Patients by the Level of Sudden Cardiac Death Risk Using the Data on Energies of Cardiac Micropotentials Obtained by Nanosensor-Based Hardware and Software Complex: Chap.; Progress in Material Science and Engineering; Vol. 351 : Studies in Systems, Decision and Control (SSDC)

Dades bibliogràfiques
Parent link:Progress in Material Science and Engineering/ eds. I. V. Minin, S. Uchaikin, A. Rogachev, O. Stary
Vol. 351 : Studies in Systems, Decision and Control (SSDC).— 2021.— [P. 217-236]
Autor corporatiu: Национальный исследовательский Томский политехнический университет Инженерная школа неразрушающего контроля и безопасности Научно-производственная лаборатория "Медицинская инженерия"
Altres autors: Avdeeva D. K. Diana Konstantinovna, Maksimov I. V. Ivan Vadimovich, Guo Wenjia, Ivanov M. L. Maxim Leonidovich, Turushev N. V. Nikita Vladimirovich, Yuzhakov M. M. Mikhail Mikhaylovich, Enshin S. I. Stepan Igorevich, Mazikov S. V. Sergey Valerjevich, Marchenko E. S. Ekaterina Sergeevna, Balakhonova M. V. Mariya Vyacheslavovna
Sumari:Title screen
The problem of human cardiovascular diseases is one of the critical health problems. The most serious aspect is sudden cardiac death (SCD). A new method and approaches to recording micropotentials by the nanosensor-based hardware and software complex are elaborated for dynamic personalized monitoring of the heart. Tables with data on functioning of the patient's heart during multiple examinations are automatically generated in one Excel file. The study presents results on cardiac micropotentials in groups 1A-volunteers died from cardiogenic shock, and 1B-volunteers with acute myocardial infarction (AMI) survived. Relative average values of the total energy for all time intervals are given in the following amplitude ranges: (0.5-1.0) µV; (1.1-3.0) µV; (3.1-5.0) µV; (5.1-20.0) µV in a dynamic pattern. The results are divided into 3 ranges: the total energy value of more than 150%, less than 50% and in the range of (50-150)%. The most significant changes in the micropotential energy by a factor of (4, 5) can be observed in the range of (5.1-20) µV. It is shown that the excess of the micropotential energy over 150% and the decrease in the micropotential energy below 50% are predictors of SCD.
Режим доступа: по договору с организацией-держателем ресурса
Idioma:anglès
Publicat: 2021
Matèries:
Accés en línia:https://doi.org/10.1007/978-3-030-68103-6_20
Format: Electrònic Capítol de llibre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665300

MARC

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200 1 |a New Approaches to Stratification of Patients by the Level of Sudden Cardiac Death Risk Using the Data on Energies of Cardiac Micropotentials Obtained by Nanosensor-Based Hardware and Software Complex  |e Chap.  |f D. K. Avdeeva, I. V. Maksimov, Guo Wenjia [et al.] 
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300 |a Title screen 
320 |a [References: 34 tit.] 
330 |a The problem of human cardiovascular diseases is one of the critical health problems. The most serious aspect is sudden cardiac death (SCD). A new method and approaches to recording micropotentials by the nanosensor-based hardware and software complex are elaborated for dynamic personalized monitoring of the heart. Tables with data on functioning of the patient's heart during multiple examinations are automatically generated in one Excel file. The study presents results on cardiac micropotentials in groups 1A-volunteers died from cardiogenic shock, and 1B-volunteers with acute myocardial infarction (AMI) survived. Relative average values of the total energy for all time intervals are given in the following amplitude ranges: (0.5-1.0) µV; (1.1-3.0) µV; (3.1-5.0) µV; (5.1-20.0) µV in a dynamic pattern. The results are divided into 3 ranges: the total energy value of more than 150%, less than 50% and in the range of (50-150)%. The most significant changes in the micropotential energy by a factor of (4, 5) can be observed in the range of (5.1-20) µV. It is shown that the excess of the micropotential energy over 150% and the decrease in the micropotential energy below 50% are predictors of SCD. 
333 |a Режим доступа: по договору с организацией-держателем ресурса 
461 |t Progress in Material Science and Engineering  |f eds. I. V. Minin, S. Uchaikin, A. Rogachev, O. Stary 
463 |t Vol. 351 : Studies in Systems, Decision and Control (SSDC)  |v [P. 217-236]  |d 2021 
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610 1 |a труды учёных ТПУ 
610 1 |a внезапная сердечная смерть 
610 1 |a микропотенциалы 
610 1 |a аппаратно-программные комплексы 
610 1 |a наносенсоры 
610 1 |a сердечно-сосудистые заболевания 
701 1 |a Avdeeva  |b D. K.  |c specialist in the field of non-destructive testing  |c Professor of Tomsk Polytechnic University, Doctor of technical sciences  |f 1943-  |g Diana Konstantinovna  |3 (RuTPU)RU\TPU\pers\33062 
701 1 |a Maksimov  |b I. V.  |g Ivan Vadimovich 
701 0 |a Guo Wenjia 
701 1 |a Ivanov  |b M. L.  |c specialist in the field of nondestructive testing  |c engineer at Tomsk Polytechnic University  |f 1987-  |g Maxim Leonidovich  |3 (RuTPU)RU\TPU\pers\34861 
701 1 |a Turushev  |b N. V.  |c specialist in the field of instrumentation  |c Engineer of Tomsk Polytechnic University  |f 1990-  |g Nikita Vladimirovich  |3 (RuTPU)RU\TPU\pers\32603 
701 1 |a Yuzhakov  |b M. M.  |c specialist in the field of non-destructive testing  |c Associate Scientist of Tomsk Polytechnic University  |f 1981-  |g Mikhail Mikhaylovich  |3 (RuTPU)RU\TPU\pers\36905 
701 1 |a Enshin  |b S. I.  |c Specialist in the field of electronics  |c Engineer of Tomsk Polytechnic University  |f 1990-  |g Stepan Igorevich  |3 (RuTPU)RU\TPU\pers\45398 
701 1 |a Mazikov  |b S. V.  |g Sergey Valerjevich 
701 1 |a Marchenko  |b E. S.  |g Ekaterina Sergeevna 
701 1 |a Balakhonova  |b M. V.  |g Mariya Vyacheslavovna 
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