Yu. V. Maslennikov, V. Yu. Slobodchikov, V. A. Krymov, Yu. V. Gulyaev
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引用次数: 0
Abstract
This article presents a review of domestic research on the development of new medical equipment and technologies using equipment and methods of detection of natural magnetic fields of biological objects. The core of the biomagnetic research technology consists of noncontact detection, special mathematical processing, and analysis of the values of the parameters of the magnetic field of the investigated bioobject (generated by the heart, brain, muscles, etc.) found in the specified points of space outside the body of the bioobject using highly sensitive magnetometer equipment, and in particular, using superconducting quantum interference devices (SQUIDs). Based on the studies of myocardial electrophysiology, the samples of technical solutions of magnetometric SQUID-systems for magnetocardiography (MCG), data on diagnostic capabilities, and prospects of practical application of MCG in cardiology are presented. The methodology of magnetocardiographic examination is described and the advantages of MCG application for early diagnosis and control of therapy of various cardiovascular diseases (CVDs) are described. The work of the software of MAG-SCAN diagnostic complexes for the analysis of magnetocardiosignals is illustrated by solving the problem of classifying groups of cardiological patients.
摘要 本文综述了国内利用生物物体天然磁场检测设备和方法开发新型医疗设备和技术的研究情况。生物磁场研究技术的核心是利用高灵敏度磁强计设备，特别是利用超导量子干涉装置（SQUID），对被调查生物物体（由心脏、大脑、肌肉等产生）在生物物体体外指定空间点的磁场参数值进行非接触式检测、特殊数学处理和分析。在心肌电生理学研究的基础上，介绍了用于磁心动图（MCG）的磁测量 SQUID 系统的技术解决方案样本、诊断能力数据以及 MCG 在心脏病学中的实际应用前景。介绍了磁心动图检查的方法，以及应用 MCG 对各种心血管疾病（CVDs）进行早期诊断和控制治疗的优势。通过解决心脏病患者群体分类问题，说明了 MAG-SCAN 诊断综合软件在分析磁心动图信号方面的工作。
期刊介绍：
The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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