Open Access e-Journal Cardiometry No.16 May 2020
We should mention that Cardiometry is a fine diagnostics tool to assess heart life expectancy. Our experts, using Cardiocode in “red zones” in intensive care units, have confirmed effectiveness of noninvasive measuring of the hemodynamics data on the cardiovascular system performance in critical patients with different severity degrees. The medical staff involved had a possibility not only to monitor the state in each critical patient, but also to predict and control the progression of a disease. We are going to publish some results of this pilot study in our next issues. We should mention that Cardiometry is a fine diagnostics tool to assess heart life expectancy. Our experts, using Cardiocode in “red zones” in intensive care units, have confirmed effectiveness of noninvasive measuring of the hemodynamics data on the cardiovascular system performance in critical patients with different severity degrees. The medical staff involved had a possibility not only to monitor the state in each critical patient, but also to predict and control the progression of a disease. We are going to publish some results of this pilot study in our next issues.
10. Altai YA, Kremlev AS. Formation of an integratedapproach to the analysis processing of the measuringelectrocardiographic information. Bulletin arrhythmology.2020(27):155. [in Russian]11. Altay YA., et al. Comparative analysis of characteristicsof electrodes to estimate accuracy in recording longtermECG signal parameters. Cardiometry. 2019(15):63-72. DOI: 10.12710/cardiometry.2019.15.6372.12. Nemirko AP, Manilo LA, Kalinichenko NA. Mathematicalanalysis of biomedical signals and data. M .:FIZMATLIT.; 2017. [in Russian]13. Altay YA, Kremlev AS. Analysis and systematizationof noise arising by long-term recording of ECG signal.ELCONRUS Intern. Conf; Univ. Eltech, Saint Petersburg,Russian Federation; 2018.14. Makarov LM, et al. National Russian guidelines onapplication of the methods of Holter monitoring in clinicalpractice. Russian Journal of Cardiology. 2014 (2):6-71.15. Orlov YN. Electrodes for the measurement of bioelectricpotentials. Moscow: MSTU named after N.E.Bauman; 2006. [in Russian]16. Grigoriev EB, Krasichkov AS, Nifontov EM. Qualificationstatistical characteristics myographic interferenceelectrocardiosignal multichannel recording.Proceedings of the Russian universities. Radionics.2018(22):118-125. DOI: 10.32603/1993-8985-2018-21-6-118-125. [in Russian]17. Zhestkova YE. Increased noise immunity transducers:Dissertation. Penza, 2005. 24 p. [in Russian]18. Karanik AA, Gavrielok YV. The computer as asource of interference. Materials of scientific-technicalconference of students and graduates "Actual problemsof energy" Minsk, 2017. p. 37-38. [in Russian]19. Tompkins WJ. Biomedical digital signal processing.New Jersey: Upper Saddle River,2000.20. Miroschnik IV. Automatic Control Theory. Linearsystems. SPb.: Piter, 2005. p. 337. [in Russian]21. Bystrov SV, Wunder AT, Ushakov AV. The decisionsignal uncertainty problems in analytical constructionserial compensator in piezo control problem. Scientificand Technical Gazette Information Technologies, Mechanicsand Optics. 2016(16):451–459. [in Russian]22. Altay YA, Kremlev AS., Margun AA. ECG SignalFiltering Approach for Detection of P, QRS, T Wavesand Complexes in Short Single-Lead Recording EL-CONRUS Intern. Conf; Univ. Eltech, Saint Petersburg,Russian Federation; 2019.23. Kalinichenko, NA., Yurieva OD. Effect ECG samplingfrequency accuracy of calculation of the spectralparameters of heart rate variability. Information andControl Systems. 2008(33):46-9. [in Russian]24. Kwon O, Jeong J. Electrocardiogram SamplingFrequency Range Acceptable for Heart Rate VariabilityAnalysis. Healthcare informatics research.2018(24):198-206. DOI: 10.4258/hir.2018.24.3.198.25. Paarman LD. Design and analysis of analog filters:a signal processing perspective. New-York: Kluwer academicpublishers, 2003.26. Lyons R. Digital Signal Processing. Translation editedby Britov AA. Moscow: Binom, 2006. 655 p. [in Russian]27. Python Graphing Library, Plotly. [Online]. Available:https://plot.ly/python/96 | Cardiometry | Issue 16. May 2020
REVIEW Submitted: 5.03.2020; Accepted: 15.04.2020; Published online: 21.05.2020Review of the recommendersystems application in cardiologyKonstantin V. Kamyshev 1,2 *, Viktor M. Kureichik 2 ,Ilya M. Borodyanskiy 21Russian New UniversityRussia, 105005, Moscow, Radio str., 222Southern Federal UniversityRussia, 344006, Rostov-on-Don, Bolshaya Sadovaya Str., 105/42*Corresponding author:e-mail: camyshevrus@gmail.comAbstractThe article provides a review of the recommender systems applicationin medical field, cardiology, in particular. The concept ofrecommender systems is defined, the brief history of the recommendersystems development is given. The main types of recommendersystems and principles of their construction are presented.The advantages and disadvantages of the recommendersystem methods application in cardiology are identified. Methodsfor improving the recommender systems are proposed.KeywordsRecommender system, Filtering, Collaborative, Content, Hybrid,information retrieval, MRS, PEHCImprintKonstantin V. Kamyshev, Viktor M. Kureichik, Ilya M. Borodyanskiy.Review of the recommender systems application in cardiology.Cardiometry; Issue 16; May 2020; p.97-105; DOI: 10.12710/cardiometry.2020.16.97105; Available from: http://www.cardiom-etry.net/issues/no16-may-2020/recommender-systems-applica-tion-in-cardiologyTopicality of the researchThis paper presents a review of the currently availableresearch in the field of big data, namely, softwaresystems, called recommender systems. To date, theprocessing of big data, in particular, the constructionof recommender systems, is one of the most promisingareas of informatics development. One of the fastestgrowing segments of the recommender algorithms applicationis the segment of medicine and public health.This review is relevant also on this basis. Novelty andfundamental difference of this review is its focus onthe implementation of the recommendations in thenarrower, but the most important branch of medicine,cardiology.IntroductionIt is not a secret that in recent years the Internethas developed by leaps and bounds: it generates vastamounts of data stored. The average user has to process,analyze and organize the data, and, above all,allocate the necessary information from the mass. Ofcourse, it is very difficult to do, because the necessaryinformation is lost among the large amounts of data.In connection with this, there are tools that can assistthe user in finding relevant data. These tools are calledrecommender systems. Recommender system is a softwarethat analyzes users requests to predict what kindof information will be of interest to a particular user ata particular time. Recommender systems show preferenceof the content for a particular user based on theinformation that the user considers relevant or basedon the processing of user’s data, such as his search queries.Recommender system made significant changesin the interaction with users. Instead of the generationof static data, the system changes, adjusts to the specificuser [1]. Recommender systems have the followingcommon characteristics: the system adapts to the individualuser; takes into account the current end-userpreferences, adjusting to them over time; constantlyfinds new information and offers it to the user. Dueto these properties, sites, based on the use of recommendersystems, are attractive to the user. Accordingly,recommender systems are interesting to the ownersof the sites themselves, because with their help theyincrease the attractiveness of the site and its content.Recommender systems have been applied in many areasof human life: search for information, commerce,social networks, medicine, etc.The main "actors" in any recommender systemsare user and item, i.e. the recipient of the recommendationsand the recommendation itself, i.e. the object,recommended to the user.The user is also a source of data about his preferences,on the basis of which the item is selected [2]. Ingeneral, the task of the recommender systems can beformulated as the "definition of the object, previouslyunknown to the user (or not used by him for anyIssue 16. May 2020 | Cardiometry | 97
- Page 47 and 48: measurement thereof cannot detect a
- Page 49 and 50: AimsThe aim of this study is to ass
- Page 51 and 52: vascular system condition, using an
- Page 53 and 54: 4. Papers devoted to the AH treatme
- Page 55 and 56: 2019;2(12):18-23. https://doi.org/1
- Page 57 and 58: ORIGINAL RESEARCH Submitted: 17.01.
- Page 59 and 60: The statistical analysis of the obt
- Page 61 and 62: Table 7Eigenvalues and percentage o
- Page 63 and 64: but also as a means for active mode
- Page 65 and 66: values [6]. Both groups are homogen
- Page 67 and 68: Table 2No. of ind.BMIAge, yearsHb,
- Page 69 and 70: ORIGINAL RESEARCH Submitted: 18.03.
- Page 71 and 72: а) b)Figure 1. Fragments of the bl
- Page 73 and 74: а)b)cd)Figure 3. Fragments of seru
- Page 75 and 76: zation of the facias and add to the
- Page 77 and 78: PV5 - blood volume (part of SV), pu
- Page 79 and 80: Conflict of interestNone declared.A
- Page 81 and 82: Issue 16. May 2020 | Cardiometry |
- Page 83 and 84: the action of high level energy. Ac
- Page 85 and 86: Figure 5. Diagram of “a multipuls
- Page 87 and 88: REPORT Submitted: 25.02.2020; Accep
- Page 89 and 90: used to suppress this sort of noise
- Page 91 and 92: tion v[n] using HFF, then the final
- Page 93 and 94: jω jω jωZe ( ) = Xe ( ) H1( e )
- Page 95 and 96: Figure 4. Filtering of the ECG sign
- Page 97: Significant data have been obtained
- Page 101 and 102: companies have even made the implem
- Page 103 and 104: the evaluation of the item occurs.
- Page 105 and 106: XFig.1. Medical recommender system
- Page 107 and 108: System Using Collaborative Filterin
- Page 109 and 110: processing analysis, in particular
- Page 111 and 112: Statement on ethical issuesResearch
- Page 113 and 114: ORIGINAL RESEARCH Submitted: 12.01.
- Page 115 and 116: Table 1Descriptive statistics of pa
- Page 117 and 118: ison to non-diabetic patients but s
- Page 119 and 120: ORIGINAL RESEARCH Submitted: 4.12.2
- Page 121 and 122: The data have been collected and th
- Page 123 and 124: It is known that the main mechanism
10. Altai YA, Kremlev AS. Formation of an integrated
approach to the analysis processing of the measuring
electrocardiographic information. Bulletin arrhythmology.
2020(27):155. [in Russian]
11. Altay YA., et al. Comparative analysis of characteristics
of electrodes to estimate accuracy in recording longterm
ECG signal parameters. Cardiometry. 2019(15):63-
72. DOI: 10.12710/cardiometry.2019.15.6372.
12. Nemirko AP, Manilo LA, Kalinichenko NA. Mathematical
analysis of biomedical signals and data. M .:
FIZMATLIT.; 2017. [in Russian]
13. Altay YA, Kremlev AS. Analysis and systematization
of noise arising by long-term recording of ECG signal.
ELCONRUS Intern. Conf; Univ. Eltech, Saint Petersburg,
Russian Federation; 2018.
14. Makarov LM, et al. National Russian guidelines on
application of the methods of Holter monitoring in clinical
practice. Russian Journal of Cardiology. 2014 (2):6-71.
15. Orlov YN. Electrodes for the measurement of bioelectric
potentials. Moscow: MSTU named after N.E.
Bauman; 2006. [in Russian]
16. Grigoriev EB, Krasichkov AS, Nifontov EM. Qualification
statistical characteristics myographic interference
electrocardiosignal multichannel recording.
Proceedings of the Russian universities. Radionics.
2018(22):118-125. DOI: 10.32603/1993-8985-2018-21-
6-118-125. [in Russian]
17. Zhestkova YE. Increased noise immunity transducers:
Dissertation. Penza, 2005. 24 p. [in Russian]
18. Karanik AA, Gavrielok YV. The computer as a
source of interference. Materials of scientific-technical
conference of students and graduates "Actual problems
of energy" Minsk, 2017. p. 37-38. [in Russian]
19. Tompkins WJ. Biomedical digital signal processing.
New Jersey: Upper Saddle River,2000.
20. Miroschnik IV. Automatic Control Theory. Linear
systems. SPb.: Piter, 2005. p. 337. [in Russian]
21. Bystrov SV, Wunder AT, Ushakov AV. The decision
signal uncertainty problems in analytical construction
serial compensator in piezo control problem. Scientific
and Technical Gazette Information Technologies, Mechanics
and Optics. 2016(16):451–459. [in Russian]
22. Altay YA, Kremlev AS., Margun AA. ECG Signal
Filtering Approach for Detection of P, QRS, T Waves
and Complexes in Short Single-Lead Recording EL-
CONRUS Intern. Conf; Univ. Eltech, Saint Petersburg,
Russian Federation; 2019.
23. Kalinichenko, NA., Yurieva OD. Effect ECG sampling
frequency accuracy of calculation of the spectral
parameters of heart rate variability. Information and
Control Systems. 2008(33):46-9. [in Russian]
24. Kwon O, Jeong J. Electrocardiogram Sampling
Frequency Range Acceptable for Heart Rate Variability
Analysis. Healthcare informatics research.
2018(24):198-206. DOI: 10.4258/hir.2018.24.3.198.
25. Paarman LD. Design and analysis of analog filters:
a signal processing perspective. New-York: Kluwer academic
publishers, 2003.
26. Lyons R. Digital Signal Processing. Translation edited
by Britov AA. Moscow: Binom, 2006. 655 p. [in Russian]
27. Python Graphing Library, Plotly. [Online]. Available:
https://plot.ly/python/
96 | Cardiometry | Issue 16. May 2020
Change language