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\title{Heart Electrical Instabilities: Some Mechanisms by Topology, Symmetry, Spin, Semiotics; Diagnosis}
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             \author[1]{Sergii K.  Kulishov}

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\date{\small \em Received: 15 December 2019 Accepted: 31 December 2019 Published: 15 January 2020}

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\begin{abstract}
        


Background or Objectives: Different technologies were used for mathematical modeling of biological rhythms, but individual diagnosis of heart rhythm and conductivity disturbance remains problem of our time. The purpose of this investigation was to formulate models, algorithms for making heart electrical instability diagnosis by topology, symmetry, spin, semiotics of electromagnetic processes. Methods: We used algorithm for diagnosing heart electrical instability, which reduces to qualitative and quantitative analysis of electrocardiograms (ECG) in standard, inverted, 3D (as rotation bodies of ECG?s elements) forms; constructing graphs, including ?Gift wrapping? algorithm; calculation distances between points, angles between graphs, and others; comparison of qualitative and quantitative characteristics of these graphs by selective multiple testing; formulation of the diagnostic conclusion. Methods:We used algorithm for diagnosing heart electrical instability, which reduces to qualitative and quantitative analysis of electrocardiograms (ECG) in standard, inverted, 3D (as rotation bodies of ECG's elements) forms; constructing graphs, including "Gift wrapping" algorithm; calculation distances between points, angles between graphs, and others; comparison of qualitative and quantitative characteristics of these graphs by selective multiple testing; formulation of the diagnostic conclusion.

\end{abstract}


\keywords{heart electrical instability, mathematical modeling, diagnosis}

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\let\tabcellsep& 	 	 		 
\section[{Introduction a) Background of the Study}]{Introduction a) Background of the Study}\par
ifferent technologies were used for mathematical modeling of biological rhythms. \hyperref[b0]{1,}\hyperref[b1]{2} Heart electrical instability has various causes. Qualitative and quantitative electrocardiogram (ECG) assessment was basis for differential diagnosis \hyperref[b2]{[3]}\hyperref[b3]{[4]}\hyperref[b4]{[5]}\hyperref[b5]{[6]}\hyperref[b6]{[7]} .\par
Author: PhD, MSc, MD, PhD, MSc Professor, Department of Internal Medicine No 1, UMSA, Street Shevchenko 23, 36011, Poltava, Ukraine. e-mail: kulishov@meta.ua b) Objectives of the Study Objects of investigation were 170 electro cardiograms with heart electrical instabilities. 
\section[{c) Specific Aims}]{c) Specific Aims}\par
The purpose of this investigation was to formulate models, algorithms for making heart electrical instability diagnosis by topology, symmetry, spin, semiotics of electromagnetic processes. 
\section[{II.}]{II.} 
\section[{Methods a) Study Variables}]{Methods a) Study Variables}\par
Our concept is presented as step by step analysis of electrical myocardial instability.\par
We were used Typical Conceptual Spaces by some principles 8 :\par
? Information is organized by quality dimensions that are sorted into domains; ? Domains are endowed with a topology or metric; ? Similarity is represented by distance in a conceptual space.\par
Properties are presented a convex region in a single domain. Concept consists from number of convex regions in different domains and information about how the regions in different domains are correlated. \hyperref[b7]{8} Concepts are equal frames and geometric structure. \hyperref[b7]{8} There are two properties of regions that are desirable: connectedness and convexity. \hyperref[b7]{8} A region is connected if it cannot be decomposed into two or smaller nonintersecting regions. \hyperref[b7]{8} It is convex if every line that connects any two points passes only through the region. \hyperref[b7]{8} The notion of a line is contextual and, hence, so is that of convexity. \hyperref[b7]{8} We proposed oxymoron fractal and anti-fractal, and Moebius strip like heart arrhythmias and blockades, including racemic form concepts, that consist from such domains (regions) Conversion of cardiac arrhythmias and blockades as fractal and/ or anti-fractal antonyms by genetic algorithm promote understanding of arrhythmogenesis, triggers and resonators of these processes; improve the quality of diagnosis as precondition to correct treatment.\par
Genetic algorithm of heart electrical instabilities diagnosis by fractal and anti-fractal analysis 11 :\par
? We take a pairs of chromosomes, consisting from fractals and/ or anti-fractals. ? Chromosome genes may be sets and anti-sets:\par
Cantor, Julia, Mandelbrot, von Koch, Sierpinski carpet, Sierpinski Triangle, Sierpinski anti-Triangle, the Sierpinski gasket, the Sierpinski anti-gasket, Peano curve, Peano anti-curve, the Hilbert curve, Darer pentagon, Cantor square, tricorn and multicorns.\par
? As a result of crossing-over (one-, two-point or multi-point), we get new offspring chromosomes consisting from different combinations of genes.\par
? New chromosomes allow to analyze physical, mathematical, biomedical data. ? Results of modeling of cardiac arrhythmias and blockades as the unity of opposites, fractal and anti-fractal antonyms, oxymorons is presented on language "Dragon".  {\ref 10} We used the quantum genetic algorithm for differential diagnosis of antonym, oxymoron like heart electrical instabilities by using of qubit chromosomes.  {\ref 12} Pecularities of Moebius strip like cardiac arrhythmias and blockages were determined by convex analysis according to such scheme 9 :\par
? initialization of cardiac electrical instability as a Moebius strip by peculiarities of atrial and ventricular depolarization and repolarization; ? convex analysis by type, volume, surface bodies of rotation electrocardiograms elements; ? convex analysis by joining of PQSRT -PQSRT complexes points according to "Gift wrapping" algorithm" 9 ; ? construction of the convex hull for determination the relationship between the investigated complexes as Moebius strip like constituents; ? making conclusions by using of conceptual spaces data. 
\section[{b) Statistical Analysis}]{b) Statistical Analysis}\par
We used of algorithm of creative solutions as derivatives of selective multiple testing  {\ref 13}   
\section[{Results}]{Results}\par
Examples of linear and nonlinear antonym pathogenesis of arrhythmias and blockades as result of unity 11 :  Rhythm and conduction disturbances can be represented by various known fractal-antifractal structural, electrical remodelling of the heart (fig.  {\ref 1}). 11 So Sierpinski napkin may reflect small and large sclerotic sclerotic processes in the myocardium, as a result of chronic and acute forms of coronary artery disease.  {\ref 11} At the same time, this kind of fractal and Cantor set may reflect multiple foci of atrial depolarization during atrial fibrillation. Koch set may be a prototype, model of CLC, WPW syndromes; left, right bundle branch blockades.  {\ref 11} Tricorn and multicorns antifractals can be a model of the pathogenesis of rhythm and conduction disorders, as a re-entry effect. 11 Examples of oxymoron pathogenesis of arrhythmias 11 :\par
The Moebius like space orientation of depolarization processes were characterized by the change of supraventricular pacemaker and ectopic activity onto the ventricular one.  {\ref 11} In the patients with sick sinus syndrome, the Moebius like arrhythmias were displayed as a combination of supraventricular and ventricular extrasystoles, pair fibrillation and flutter transformation from atria to ventricles.  {\ref 11} Qualitative and quantitative characteristics of 2D, 3D electrocardiograms in the patients with pirouette ventricular pair extrasystoles, pirouette ventricular tachycardia, vicarious rhythms as result of sinus node dysfunction, binodal syndrome gave us possibilities to determine peculiarities of oxymoron, fractal and antifractal, racemic Moebius strip like transitions and iteration (fig. \hyperref[fig_2]{2,3}).  {\ref 14,} {\ref 15} IV. 
\section[{Discussion}]{Discussion}\par
The proposed technology of solving clinical problems by system and anti-system comparison, presented as a graphical model and program by languages "Dragon", promotes understanding of complex principles of clinical medicine, improve the quality of diagnosis as precondition to change of the treatment. 11 Electrical instability of the heart is derived from its structural and electrical remodeling. 11 Rhythm and conduction disturbances can be represented by various known fractals and anti-fractals.  {\ref 11} Characteristics of volume, surface, laminar and turbulent data, spin, chirality of rotation bodies of electrocardiogram elements give us possibilities to determine depolarization and repolarization electromagnetic picture, oxymoron, fractal and anti-fractal, Moebius strip like transitions and iteration, state of electrical heart instabilities. 11 V. 
\section[{Conclusion and Implications for Translation}]{Conclusion and Implications for Translation}\par
Our investigation give us possibilities to formulate models, algorithms for making heart electrical instability diagnosis by topology, symmetry, spin, semiotics of electromagnetic processes.\par
Understanding electrical myocardial instability mechanisms improve the quality of diagnosis as precondition to treatment correction. 
\section[{Compliance with Ethical Standards}]{Compliance with Ethical Standards}\par
Conflicts of Interest: Author state that there are no conflicts of interest to report. The work done by the author, there is no fund received from any agency or company at all. Financial Disclosure: The work done by the author, there is no fund received from any agency or company at all. 
\section[{Ethics Approval: Complaince with Ethical Standards}]{Ethics Approval: Complaince with Ethical Standards} 
\section[{Key Messages}]{Key Messages}\par
Understanding electrical myocardial instability mechanisms improve the quality of diagnosis as precondition to treatment correction.  \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 1 :Fig. 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.8282051282051281\textwidth}P{0.021794871794871794\textwidth}}
discovery rate, q-value. Determination of the sensitivity\tabcellsep \\
and specificity of these variabilities.\tabcellsep \\
ii. Secondary screening the variabilities for multiple\tabcellsep \\
test methods\tabcellsep \\
B1: These numerical dependent variabilities with P = .05\tabcellsep \\
or less, and / or minimal false discovery rate, with high\tabcellsep \\
sensitivity and specificity by diagnostic capabilities must\tabcellsep \\
use for formation of new variabilities as descendants of\tabcellsep \\
2, 3, 4.. n numerical dependent variabilities as the\tabcellsep \\
derivatives of various mathematical transformations as\tabcellsep \\
Cantor, Sierpinski, von Koch sets, etc., anti-fractal sets;\tabcellsep \\
Moebius strip like aggregates, oxymoron combinations;\tabcellsep \\
and others mathematical transformations derivatives.\tabcellsep \\
iii. Check the newly formed variabilities similar to step\tabcellsep \\
A to estimate the effectiveness of such changes.\tabcellsep \\
iv. Comparison of multiple testing of more informative\tabcellsep \\
primary and secondary variabilities by accuracy,\tabcellsep \\
sensitivity possibilities. v. If it's necessary, the search of new selection and specificity of diagnostic principles of variabilities for multiple testing must be continued. c) Ethical Approval Complaince with Ethical Standards III.\tabcellsep Volume XX Issue VI Version I\\
:\tabcellsep ( D D D D ) K\\
i. Initial selection of multiple testing methods; A1: Selection of independent and dependent variability; Calculating the of mean, standard error of mean, standard deviation, 95\% confidence interval for mean, median, minimum, maximum, range, quartiles; Determination of the variabilities distribution -parametric or nonparametric by single-factor the\tabcellsep Medical Research\\
\multicolumn{2}{l}{Kolmogorov-Smirnov test; Shapiro-Wilk W test and graphical methods: frequency distribution histograms stem \& leaf plots; scatter plots; box \& whisker plots; normal probability plots: PP and QQ plots; graphs with error bars (Graphs: Error Bar). A2: ANOVA (Analysis of Variance) test is used for parametric variabilities distribution. If deviations are homogeneous by Levene test would used the method of multiple comparison groups by Tukey HSD, Scheffe, Bonferroni, and in the cases without homogeneity we must use the criteria Tamhane's T2, Games-Howell; Kruskal-Wallis test, nonparametric equivalent of the ANOVA, is used for nonparametric variabilities distribution; A3: Global Journal of}\\
© 2020 Global Journals\tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 			\footnote{Heart Electrical Instabilities: Some Mechanisms by Topology, Symmetry, Spin, Semiotics; Diagnosis} 			\footnote{K © 2020 Global Journals January 2019; Madrid, Spain; IEEE Computer Society; Conference Publishing Services; 2019, December 30; The Institute of Electrical and} 			\footnote{© 2020 Global JournalsHeart Electrical Instabilities: Some Mechanisms by Topology, Symmetry, Spin, Semiotics; Diagnosis} 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
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