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H R, Bhat VR, H A. Forward problem of electrocardiography based on cardiac source vector orientations. Biomed Phys Eng Express 2024; 10:035036. [PMID: 38626731 DOI: 10.1088/2057-1976/ad3f20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/16/2024] [Indexed: 04/18/2024]
Abstract
To localize the unusual cardiac activities non-invasively, one has to build a prior forward model that relates the heart, torso, and detectors. This model has to be constructed to mathematically relate the geometrical and functional activities of the heart. Several methods are available to model the prior sources in the forward problem, which results in the lead field matrix generation. In the conventional technique, the lead field assumed the fixed prior sources, and the source vector orientations were presumed to be parallel to the detector plane with the unit strength in all directions. However, the anomalies cannot always be expected to occur in the same location and orientation, leading to misinterpretation and misdiagnosis. To overcome this, the work proposes a new forward model constructed using the VCG signals of the same subject. Furthermore, three transformation methods were used to extract VCG in constructing the time-varying lead fields to steer to the orientation of the source rather than just reconstructing its activities in the inverse problem. In addition, the unit VCG loop of the acute ischemia patient was extracted to observe the changes compared to the normal subject. The abnormality condition was achieved by delaying the depolarization time by 15ms. The results involving the unit vectors of VCG demonstrated the anisotropic nature of cardiac source orientations, providing information about the heart's electrical activity.
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Affiliation(s)
- Reshma H
- Department of Electronics and Communication Engineering, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal-576104, India
| | - Vikas R Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal-576104, India
| | - Anitha H
- Department of Electronics and Communication Engineering, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal-576104, India
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Kijonka J, Vavra P, Penhaker M, Bibbo D, Kudrna P, Kubicek J. Present results and methods of vectorcardiographic diagnostics of ischemic heart disease. Comput Biol Med 2024; 169:107781. [PMID: 38103481 DOI: 10.1016/j.compbiomed.2023.107781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/03/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
This article presents an overview of existing approaches to perform vectorcardiographic (VCG) diagnostics of ischemic heart disease (IHD). Individual methodologies are divided into categories to create a comprehensive and clear overview of electrical cardiac activity measurement, signal pre-processing, features extraction and classification procedures. An emphasis is placed on methods describing the electrical heart space (EHS) by several features extraction techniques based on spatiotemporal characteristics or signal modelling and signal transformations. Performance of individual methodologies are compared depending on classification of extent of ischemia, acute forms - myocardial infarction (MI) and myocardial scars localization. Based on a comparison of imaging methods, the advantages of VCG over the standard 12-leads ECG such as providing a 3D orthogonal leads imaging, better performance, and appropriate computer processing are highlighted. The issues of electrical cardiac activity measurements on body surface, the lack of VKG databases supported by a more accurate imaging method, possibility of comparison with the physiology of individual cases are outlined as potential reserves for future research.
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Affiliation(s)
- Jan Kijonka
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17.listopadu 15, Ostrava, Poruba, 708 00, Czech Republic.
| | - Petr Vavra
- Department of Surgical Studies, Faculty of Medicine of the University of Ostrava, Syllabova 19, 703 00, Ostrava 3, Czech Republic; Surgery Clinic, University Hospital Ostrava, 17. listopadu 13, Ostrava, Czech Republic.
| | - Marek Penhaker
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17.listopadu 15, Ostrava, Poruba, 708 00, Czech Republic; Faculty of Electrical Engineering and Information Technology, University of Zilina, Zilina, Czech Republic.
| | - Daniele Bibbo
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra, 62, 00146, Rome, Italy.
| | - Petr Kudrna
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Nam. Sitna 3105, 272 01, Kladno, Czech Republic.
| | - Jan Kubicek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17.listopadu 15, Ostrava, Poruba, 708 00, Czech Republic.
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Wang W, Weng F, Zhu J, Li Q, Wu X. An Analytical Approach for Temporal Infection Mapping and Composite Index Development. MATHEMATICS 2023; 11:4358. [DOI: 10.3390/math11204358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Significant and composite indices for infectious disease can have implications for developing interventions and public health. This paper presents an investment for developing access to further analysis of the incidence of individual and multiple diseases. This research mainly comprises two steps: first, an automatic and reproducible procedure based on functional data analysis techniques was proposed for analyzing the dynamic properties of each disease; second, orthogonal transformation was adopted for the development of composite indices. Between 2000 and 2019, nineteen class B notifiable diseases in China were collected for this study from the National Bureau of Statistics of China. The study facilitates the probing of underlying information about the dynamics from discrete incidence rates of each disease through the procedure, and it is also possible to obtain similarities and differences about diseases in detail by combining the derivative features. There has been great success in intervening in the majority of notifiable diseases in China, like bacterial or amebic dysentery and epidemic cerebrospinal meningitis, while more efforts are required for some diseases, like AIDS and virus hepatitis. The composite indices were able to reflect a more complex concept by combining individual incidences into a single value, providing a simultaneous reflection for multiple objects, and facilitating disease comparisons accordingly. For the notifiable diseases included in this study, there was superior management of gastro-intestinal infectious diseases and respiratory infectious diseases from the perspective of composite indices. This study developed a methodology for exploring the prevalent properties of infectious diseases. The development of effective and reliable analytical methods provides special insight into infectious diseases’ common dynamics and properties and has implications for the effective intervention of infectious diseases.
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Affiliation(s)
- Weiwei Wang
- School of Medicine, Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Data Mining Research Center, Xiamen University, Xiamen 361005, China
| | - Futian Weng
- School of Medicine, Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Data Mining Research Center, Xiamen University, Xiamen 361005, China
| | - Jianping Zhu
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Data Mining Research Center, Xiamen University, Xiamen 361005, China
- School of Management, Xiamen University, Xiamen 361005, China
| | - Qiyuan Li
- School of Medicine, Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Xiaolong Wu
- School of Medicine, Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Data Mining Research Center, Xiamen University, Xiamen 361005, China
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