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Article

Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal

1
School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2544; https://doi.org/10.3390/app9122544
Submission received: 23 May 2019 / Revised: 13 June 2019 / Accepted: 17 June 2019 / Published: 21 June 2019
(This article belongs to the Section Applied Biosciences and Bioengineering)

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.
Keywords: CHF; pattern recognition; HRV; SFS; leave-one-subject-out validation CHF; pattern recognition; HRV; SFS; leave-one-subject-out validation

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MDPI and ACS Style

Hua, Z.; Chen, C.; Zhang, R.; Liu, G.; Wen, W. Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal. Appl. Sci. 2019, 9, 2544. https://doi.org/10.3390/app9122544

AMA Style

Hua Z, Chen C, Zhang R, Liu G, Wen W. Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal. Applied Sciences. 2019; 9(12):2544. https://doi.org/10.3390/app9122544

Chicago/Turabian Style

Hua, Zhengchun, Chen Chen, Ruiqi Zhang, Guangyuan Liu, and Wanhui Wen. 2019. "Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal" Applied Sciences 9, no. 12: 2544. https://doi.org/10.3390/app9122544

APA Style

Hua, Z., Chen, C., Zhang, R., Liu, G., & Wen, W. (2019). Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal. Applied Sciences, 9(12), 2544. https://doi.org/10.3390/app9122544

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