Cluster analysis of cardiac data
The article includes the observation of the cluster analysis of medicaldata on the example of the cardiac data. One of the main effective and commonly used Data Mining methods that applied to the large amounts of information (for example, mathematical economics) are clustering methods: the search...
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| Format: | Article |
| Language: | Russian |
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Plekhanov Russian University of Economics
2018-06-01
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| Series: | Статистика и экономика |
| Subjects: | |
| Online Access: | https://statecon.rea.ru/jour/article/view/1252 |
| _version_ | 1826559690748723200 |
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| author | E. Y. Zimina |
| author_facet | E. Y. Zimina |
| author_sort | E. Y. Zimina |
| collection | DOAJ |
| description | The article includes the observation of the cluster analysis of medicaldata on the example of the cardiac data. One of the main effective and commonly used Data Mining methods that applied to the large amounts of information (for example, mathematical economics) are clustering methods: the search for signs of similarity between objects in the study of the subject area and the subsequent merger of objects into subsets (clusters) according to the established affinity. The main purpose of the investigation is to examine the hypothesis of the possibility of diagnosing the patient health status, as well as identifying his pathologies, using the analysis of electrocardiogram (ECG) series and the allocation of similar clusters based on the results of this analysis. However, the subject of clustering techniques implementation to the ECG on the grounds of similarity of forms have not previously been extensively investigated. In the model of the heart, which is used in this study, the state of the heart is taken as a fixed oscillatory process of the phenomenon of the FPU auto-return. But, on the other hand, since the heart is an self-oscillating system and it has no need to start the oscillations by obtaining the energy of “perturbation”, the concept of FPU autoreturn is introduced in the study of the heart. The mathematical modeling of the heart work by using a decomposition of the Fermi-Pasta-Ulam (FPU) was investigated. The formal description of the mathematical model of the heart as a system of connected cells myocytes is presented. This represents a single oscillatory degree of freedom described by a system of coupled nonlinear differential equations of the second order equation of Van der Pol. Cluster analysis bases on the search of similar clusters of Fourier spectrum which are received by FPU recurrence. The current results that are obtained show that the hypothesis is confirmed. In mathematical modeling of the FPU heart modeling, which is based on the forms of Fourier spectra, were identified. Subsets were identified, among which various subsets of both forms of Fourier spectra with pathologies and forms of the Fourier spectrum of healthy people were formed. From this study it follows that the cluster analysis of the electrocardiogram may refer this ECG to any cluster and thereby diagnose the state of cardiac health of the patient. |
| first_indexed | 2024-03-12T04:21:25Z |
| format | Article |
| id | doaj.art-ddb702a38c0e4d2083c4258ba2b81f8f |
| institution | Directory Open Access Journal |
| issn | 2500-3925 |
| language | Russian |
| last_indexed | 2025-03-14T09:04:24Z |
| publishDate | 2018-06-01 |
| publisher | Plekhanov Russian University of Economics |
| record_format | Article |
| series | Статистика и экономика |
| spelling | doaj.art-ddb702a38c0e4d2083c4258ba2b81f8f2025-03-02T12:41:01ZrusPlekhanov Russian University of EconomicsСтатистика и экономика2500-39252018-06-01152303710.21686/2500-3925-2018-2-30-371154Cluster analysis of cardiac dataE. Y. Zimina0Higher School of EconomicsThe article includes the observation of the cluster analysis of medicaldata on the example of the cardiac data. One of the main effective and commonly used Data Mining methods that applied to the large amounts of information (for example, mathematical economics) are clustering methods: the search for signs of similarity between objects in the study of the subject area and the subsequent merger of objects into subsets (clusters) according to the established affinity. The main purpose of the investigation is to examine the hypothesis of the possibility of diagnosing the patient health status, as well as identifying his pathologies, using the analysis of electrocardiogram (ECG) series and the allocation of similar clusters based on the results of this analysis. However, the subject of clustering techniques implementation to the ECG on the grounds of similarity of forms have not previously been extensively investigated. In the model of the heart, which is used in this study, the state of the heart is taken as a fixed oscillatory process of the phenomenon of the FPU auto-return. But, on the other hand, since the heart is an self-oscillating system and it has no need to start the oscillations by obtaining the energy of “perturbation”, the concept of FPU autoreturn is introduced in the study of the heart. The mathematical modeling of the heart work by using a decomposition of the Fermi-Pasta-Ulam (FPU) was investigated. The formal description of the mathematical model of the heart as a system of connected cells myocytes is presented. This represents a single oscillatory degree of freedom described by a system of coupled nonlinear differential equations of the second order equation of Van der Pol. Cluster analysis bases on the search of similar clusters of Fourier spectrum which are received by FPU recurrence. The current results that are obtained show that the hypothesis is confirmed. In mathematical modeling of the FPU heart modeling, which is based on the forms of Fourier spectra, were identified. Subsets were identified, among which various subsets of both forms of Fourier spectra with pathologies and forms of the Fourier spectrum of healthy people were formed. From this study it follows that the cluster analysis of the electrocardiogram may refer this ECG to any cluster and thereby diagnose the state of cardiac health of the patient.https://statecon.rea.ru/jour/article/view/1252clusteringdigital economycardiologybig datamathematical modeling |
| spellingShingle | E. Y. Zimina Cluster analysis of cardiac data Статистика и экономика clustering digital economy cardiology big data mathematical modeling |
| title | Cluster analysis of cardiac data |
| title_full | Cluster analysis of cardiac data |
| title_fullStr | Cluster analysis of cardiac data |
| title_full_unstemmed | Cluster analysis of cardiac data |
| title_short | Cluster analysis of cardiac data |
| title_sort | cluster analysis of cardiac data |
| topic | clustering digital economy cardiology big data mathematical modeling |
| url | https://statecon.rea.ru/jour/article/view/1252 |
| work_keys_str_mv | AT eyzimina clusteranalysisofcardiacdata |