This report discusses the employment of the electrical characteristics of biologically active points (BAP) in the meridian of the heart to solve the task of prognosticating recurrence of myocardial infarctions during the rehabilitation period using fuzzy decision rule

Ivanov, V. A.

Kolesnik, A. I.

Korenevskiy, N. A.

Makkonen, K. F.

Seregin, S. P.

English

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IntroductionStudies by researchers of different scientific schoolshave shown that reflex diagnosis may provide an approachto the prognostication and early diagnosis of diseases,including diseases of the heart and cardiovascular system;these are based on measurement of the electrical properties of biologically active points (BAP) [15]. One advantage of reflex diagnosis methods is that the responses ofbiologically active points to changes in the internal structures of the body occur before clinical symptoms of disease are manifest. This provides for the detection andtreatment of diseases at their earliest stages of development and, sometimes, their prevention by application ofprophylactic measures. The medicaltechnical and timecosts of organizing investigations are significantly lowerthan those of conventional approaches [2, 6].In tasks in which the information value of BAP isinsufficient for the accuracy required, this can beobtained using various signs used in conventional medicine aggregated into a final decision rule using hybridfuzzy models, the general theory of whose constructionhas been laid out in [3, 7, 8].The total cost of achieving the necessary prognosticand diagnostic accuracy is generally lower than whenstandard study methods are used.MethodsOne of the most important tasks in constructingdecisionmaking models is that of selecting informativeproperties, particularly informative BAP.The procedure of selecting informative BAP will bemore effective if the characteristics of the production ofinformation on the state of the body’s internal structuresat these points are considered. These characteristicsinclude the production of large amounts of information atone point (multiple diagnoses, symptoms, syndromes);cyclic changes in the energy status of BAP during the dayeven in conditions of normal energy balance along meridians; the large volume of data required for analysis if theBiomedical Engineering, Vol. 52, No. 1, May, 2018, pp. 6871. Translated from Meditsinskaya Tekhnika, Vol. 52, No. 1, Jan.Feb., 2018, pp. 5053.Original article submitted June 26, 2017.6800063398/18/52010068 © 2018 Springer Science+Business Media, LLC1 Southwest State University, Kursk, Russia; Email: kstubmi@yandex.ru2 Kursk State University, Kursk, Russia.3 Kursk State Medical University, Kursk, Russia.4 Belgorod State University, Belgorod, Russia.5 National Research Nuclear University MEPhI, Moscow, Russia.6 Peoples’ Friendship University of Russia, Moscow, Russia.* To whom correspondence should be addressed.Prognosis of Recurrent Myocardial Infarction Based on Shortliffe Fuzzy Models Using the ElectricalCharacteristics of Biologically Active PointsN. A. Korenevskiy1*, S. P. Seregin1, V. A. Ivanov2, A. I. Kolesnik3, G. V. Siplivy3, K. F. Makkonen4, V. V. Dmitrieva5, D. I. Kicha6, and D. A. Zubarev1This report discusses the employment of the electrical characteristics of biologically active points (BAP) in themeridian of the heart to solve the task of prognosticating recurrence of myocardial infarctions during the rehabilitation period using fuzzy decision rules. Employment of only the electrical characteristics of informative BAPwas found to provide a reliability level of greater than 0.85 in decisionmaking, with a significant uncertaintyzone. If additional features characterizing longterm psychoemotional tension − lipid peroxidation and antioxidant activity − are included, the reliability of prognoses of recurrence of myocardial infarction during remissionreaches 0.95.DOI 10.1007/s10527-018-9784-1Prognosis of Recurrent Myocardial Infarction Based on Shortliffe Fuzzy Models 69pathology is not already known [13]. These properties ofthe information available at a BAP hinder the procedureof selecting the informative points in studies using conventional methods of information theory and imagerecognition theory.Considering the characteristics of the presentation ofinformation on the state of the human body at biologically active points, reports [2, 3] propose methods and algorithms for finding combinations of BAP whose analysisconfirms the situation being studied (the diagnosis) andrejects “interfering” situations “displayed” at BAP on thebasis of reference data but not present in the subject.These combinations are termed diagnostically significantpoints (DSP) [2], 3].Specially conducted studies on the prognosticationand early and differential diagnosis of diseases of the cardiovascular system, gastrointestinal tract, nervous system, musculoskeletal system, respiratory system, andothers have shown that the use of DSP in combinationwith other informative properties allows decision rulesproviding high classification quality to be developed [2,3].Considering the recommendations in [2, 5], theenergy characteristics of BAP were taken as their electrical resistance measured using a 1kHz alternating currentat a current intensity of no greater than 10 μA [2, 5].These studies showed that interval assessment of resistance ΔRj,k giving the upper and lower boundaries of resistance for interval k for point j and the relative deviations ofongoing values of the resistance of BAP Rj from nominalvalues are highly informative.Experience in solving a variety of prognosticationand diagnostic tasks using information on the energy status of BAP has shown that the properties measured areincomplete and unclear in relation to the tasks beingaddressed. In these conditions, following recommendations in [3, 7, 8] led to determination of a fuzzy logic fordecisionmaking and, in particular, the authors’ development of a method of constructing sets of hybrid fuzzydecision rules.In this study, working with the recommendations in[3, 8], the prognostication task was evaluated as a classification task with two classes: 1) the subject is not going tofall ill in the next T years (class ω0); 2) the subject will fallill in the next T years (class ωl).For selection of informative biologically active pointsand other informative parameters, highly qualifiedexperts worked on those which, when recorded, increasedconfidence in the validity of the test hypothesis (disease,ωl). In these conditions, according to recommendationsin [3, 4, 9], the starting formula for calculating the confidence coefficient (CC) of decisionmaking in relation tohypothesis ωl should be the Shortliffe cumulative iterativeformula:CCωl(i + 1) = CCωl(i) + CC*ωl(xi+l)[1 − CCωl(i)],   (1)where CCωl(i) is the coefficient of confidence in hypothesis ωl with the condition that verification of the hypothesishas already involved i informative properties; CC*ωl(xi+l) isthe coefficient of confidence in diagnosis (prognosis) ωlusing only one property xi+l; CCωl(1) = CC*ωl(x1).In a number of applications, including the taskaddressed in the present study, the coefficient of confidence can conveniently be calculated using the membership function μωl(xi+l) for the class ωl calculated for thestarting variable (xi+l) [24].The prognostic confidence in the values of the interval assessments (ranges of resistances) ΔRj,k (where j is thenumber of the BAP and k is the number of the range ΔRj)is the basis forming the diagnostic assessment and is calculated by transformation of Eq. (1) intoCCωl( j + 1) = CCωl( j) + CC*ωl(ΔRj+l,k)[1 − CCωl( j)],  (2)The technical means for recording BAP resistanceconsisted of a computer device exchanging data with thePC via Bluetooth. The device operates in two modes:search mode using sound, light, and graphical recordingof BAP and measuring mode at a frequency of 1 kHz at acurrent of 5 μA.A version of the technical implementation of thedevice is described in [6].ResultsAt the first stage of the study, an atlas of meridiansdescribed in [1] was used to select a list of BAP related todiseases of the cardiovascular system, from which, on thebasis of recommendations in [1, 5], BAP associated withthe cardiac meridian (C1C9) were used to determine therisk of developing myocardial infarction (MI). The sympathetic heart meridian V15 and ear points AP19, AP21,AP60, AP100, AP105, and AO115 were excluded becauseof low informativeness and the inconvenience of measuring their characteristics.A total of 40 patients with MI were observed.Observations of changes in patients’ status were madeover the period of a year. The study groups included people with increases in the energy characteristics of themajor points of the heart meridian. The energy characteristics of BAP were monitored monthly. People showing atleast a small increase in the resistance of the major BAP70 Korenevskiy et al.(more than 10% of nominal) without therapeutic orhealthcare measures were assigned to the class of patientswith high risk of onset and development of recurrent MI.At the end of the oneyear observation period, 94% ofsubjects showed the initial clinical signs of deteriorationin the activity of the cardiovascular system [4].At the second stage of the study, the Kullback measure of informativeness was determined with a system ofgraduated features, as described in [2, 5].Kullback analysis of informativeness allowed selectionof six BAP for decision rule (2) from the overall list: C4,C6, C7, C8, and C9 (point numbers as described in [1]).Our statistical studies showed that the system of graduations described in [5] worked adequately for identifyingcardiac and cardiovascular system pathology. In the tasksof prognosticating MI, as demonstrated by the corresponding statistical studies, this graduation was very rough, notproviding the required quality of decisionmaking.Considering this, the gradation system was modifiedas follows: >500 kΩ – 0; 400500 kΩ – 1; 300399 kΩ –2; 200299 kΩ – 3; 100199 kΩ – 4; 9099 kΩ – 5; 8089 kΩ – 6; 7079 kΩ – 7; 6069 kΩ – 8; 5059 kΩ – 9;4049 kΩ – 10; <40 kΩ – 11.The unevenness in the graduated scale is due to different information values in relation to the classificationof the states under study.Data from one year of studying patients with acutemyocardial infarction undergoing rehabilitation in medical institutions in Kursk were used to construct a table ofcoefficients of confidence (CCjr) for each of the BAPrecorded ( j is the number of the BAP and r is the numberof the gradation) (Table 1). The table is based on distribution histograms of gradations of features in terms of theclass ωMI. In constructing the table, the recommendations in [2, 3] were taken into account.These coefficients determine the prognostic abilityof each BAP electrical resistance gradation in relation tothe possible occurrence of the target class of diseases.The overall confidence that a recurrence of MI willoccur is determined by Eq. (2). Analysis of the data presented in Table 1 shows that a drop in BAP resistance tobelow 60 kΩ provides a reliable (CCMI > 0.92) prognosisof the occurrence of recurrent myocardial infarction.For the range 6090 kΩ, there is a zone of indeterminacy in decisionmaking which could be significantlydecreased by using additional informative features.In the present study, the following were selected atthe expert level: the level of longterm psychoemotionaltension – x1, antioxidant activity – x2, and lipid peroxidation – x3.The level of longterm psychoemotional tension wasdetermined as described in [2, 3]. Properties x2 and x3TABLE 1. Prognostic Table for the ωMI Class40050000000300399000002002990000.10.110029900.100.40.190990.20.300.20.180890.40.500.20.270790.50.60.20.20.260690.60.70.30.20.250590.70.80.50.20.340490.90.90.80.20.3<400.950.950.950.20.3Resistance ranges, kΩBAPС*9С*7С8С4С6>50000000* BAP forming groups of features excluding all “interfering” situations (DSP BAP).μωMI(x1)Fig. 1. Plot of ωMI membership function for the basic variable x1.μωMI(x2)Fig. 2. Plot of ωMI membership function for the basic variable x2.Prognosis of Recurrent Myocardial Infarction Based on Shortliffe Fuzzy Models 71were measured by conventional laboratory analyticalmethods.Using the recommendations described in [24],membership functions μωMI(xj) were constructed to relatex1, x2, and x3 to the class of “high risk of developingmyocardial infarction (ωMI) during remission.” A plot ofthe membership function μωMI(x2) is shown in Fig. 1.Features x2 and x3 were determined, as recommended in [4], byx2 = _______ ⋅100%;  x3 = _______ ⋅100%,       where xPH and xAH are lipid peroxidation and antioxidantactivity measured in a representative group of healthysubjects, and xPT = x19 and xAT = x20 are lipid peroxidationand antioxidant activity in the test patient.Plots of the corresponding membership functions areshown in Figs. 2 and 3.The final confidence that the subject will developmyocardial infarction is determined by Eq. (1), in whichCCωMI(1) = CCωl, determined by Eq. (2);CC*ωMI(2) = μωMI(x1);CC*ωMI(3) = μωMI(x2);CCωMI(4) = μωMI(x3).Results from mathematical modeling and statisticalcalculations for representative control cohorts showedthat use of all four components gave a level of confidencein the correct prognosis of the occurrence of myocardialinfarction during remission of 0.95.REFERENCES1. Luvsan, G., Tradition and Current Aspects of Eastern ReflexTherapy [in Russian], Nauka, Moscow (1986).2. Korenevskiy, N. A., Krupchatnikov, R. A., and Al’Kasasbekh, R. T.,Theoretical Grounds for the Biophysics of Acupuncture with Applications to Medicine, Psychology, and Ecology Based on Fuzzy Network Models. A Monograph [in Russian], TNT, Staryi Oskol (2013).3. Korenevskiy, N. A., Shutkin, A. N., Gorbatenko, S. A., andSerebrovskii, V. I., Assessment and Control of the State of Health inStudents Based on Hybrid intelligent Technologies. A Monograph[in Russian], TNT, Staryi Oskol (2016).4. Seregin, S. P., Vorob’eva, O. M., and Korenevskaya, S. N.,Mathematical Models for the Prognostication and Prophylaxis ofRecurrent Myocardial Infarction during the Rehabilitation Period.A Monograph [in Russian], N. A. Korenevskiy (scientific editor),Southwest State University, Kursk (2015).5. Portnov, F. P., Electropuncture Reflex Therapy [in Russian],Zinante, Riga (1980).6. Korenevskiy, N. A., Mukhataev, Yu. B., Startsev, E. A., andLazurina, L. P., “A multichannel analyzer of the meridional structures of the body based on AFE analog interfaces,” Med. Tekh.,No. 6, 2831 (2016).7. Korenevskiy, N. A., “The use of fuzzy decisionmaking logic formedical expert systems,” Med. Tekh., No. 1, 3335 (2015).8. Korenevskiy, N. A., “A method for constructing heterogeneousfuzzy rules for the analysis and control of the state of biotechnicalsystems,” Med. Priborostr., No. 2, 99103 (2013).xAH − xATxAHxPH − xPTxPHμωMI(x3)Fig. 3. Plot of ωMI membership function for the basic variable x3.

Prognosis of recurrent myocardial infarction based on shortliffe fuzzy models using the electrical characteristics of biologically active points

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Prognosis of recurrent myocardial infarction based on shortliffe fuzzy models using the electrical characteristics of biologically active points

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