Using ordinal partition transition networks to analyze ECG data
| Autor: | Jeremy M. Chobot, Helena R. Freitas, Christopher W. Kulp, Gene D. Sprechini |
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| Rok vydání: | 2016 |
| Předmět: |
Ordinal data
Male Databases Factual Quantitative Biology::Tissues and Organs Physics::Medical Physics General Physics and Astronomy 01 natural sciences Ordinal regression 010305 fluids & plasmas Pattern Recognition Automated Electrocardiography 0103 physical sciences Statistics Entropy (information theory) Humans Statistical analysis 010306 general physics Mathematical Physics Ordinal pattern Mathematics Applied Mathematics Significant difference Models Cardiovascular Statistical and Nonlinear Physics Female Neural Networks Computer |
| Zdroj: | Chaos (Woodbury, N.Y.). 26(7) |
| ISSN: | 1089-7682 |
| Popis: | Electrocardiogram (ECG) data from patients with a variety of heart conditions are studied using ordinal pattern partition networks. The ordinal pattern partition networks are formed from the ECG time series by symbolizing the data into ordinal patterns. The ordinal patterns form the nodes of the network and edges are defined through the time ordering of the ordinal patterns in the symbolized time series. A network measure, called the mean degree, is computed from each time series-generated network. In addition, the entropy and number of non-occurring ordinal patterns (NFP) is computed for each series. The distribution of mean degrees, entropies, and NFPs for each heart condition studied is compared. A statistically significant difference between healthy patients and several groups of unhealthy patients with varying heart conditions is found for the distributions of the mean degrees, unlike for any of the distributions of the entropies or NFPs. |
| Databáze: | OpenAIRE |
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