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• W2952583617 abstract "Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: 1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; 2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals–8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (K = 1) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application." @default.
• W2952583617 created "2019-06-27" @default.
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• W2952583617 date "2019-06-21" @default.
• W2952583617 modified "2023-10-15" @default.
• W2952583617 title "Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal" @default.
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• W2952583617 doi "https://doi.org/10.3390/app9122544" @default.
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