# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
## 6 Conclusions  
This paper proposed two models for automatic CHF diagnosis. Model A was used for distinguishing CHF patients from normal people (binary classification), and model B was used  
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Date** & **Study** & **CL** & **Dataset** & **FS** & **NF** & **Classifier** & **Acc (\%)** \\ \hline
2003 [21] & [PERSON] & 2 & \(74\times 1\times 24\) h & – & 9 & [PERSON] & 93.24 \\
2007 [22] & [PERSON] & 2 & \(83\times 1\times 5\) min & GA & \(\geq\)8 & KNN & 96.39 \\
2011 [40] & [PERSON] & 2 & \(83\times 1\times 24\) h & ESM & 6 & DT & 96.40 \\
2012 [23] & [PERSON] & 2 & \(83\times 1\times 68\) min & GA & 16 & SVM & 98.79 \\
2013 [4] * & [PERSON] & 2 & \(44\times 1\times 24\) h & ESM & 7 & DT & 85.40 \\
2014 [32] & [PERSON] & 2 & \(83\times 1\times 5\) min & SBS & 27 & SVM & 91.56 \\
2014 [43] * & [PERSON] & 3 & Non-uniform & – & 5 & CART & 81.80\% \\
2015 [26] * & [PERSON] & 2 & \(44\times 1\times 24\) h & GDA & 1 & KNN & 100 \\
2016 [24] & [PERSON] & 2 & Non-uniform & RM & 22 & SVM & 97.60 \\
2016 [27] * & [PERSON] & 4 & \(116\times 1\times 24\) h & SBS & 180 & DT-SVM & 96.61 \\
2017 [25] & [PERSON] & 2 & \(107\times 1\times 24\) h & RM & 10 & Ensemble & 98.10 \\
2018 [42] & [PERSON] & 2 & Non-uniform & – & 1 & CNN & 81.85 \\
2019 [28] * & [PERSON] & 4 & Non-uniform & – & 20 & CNN & 97.60 \\ Our work & 2 & \(113\times 1\times 8\) h & SPS & 5 & SVM & 97.35 \\ Our work & 4 & \(41\times 1\times 8\) h & SPS & 4 & KNN & 87.80 \\ \hline \hline \end{tabular}  
* CL: classification level; FS: feature selection; NF: the number of features; Acc: accuracy. For feature selection algorithms, GA: genetic algorithm; ESM: exhaustive search method; SBS: sequence backward selection; GDA: generalized discriminant analysis; RM: ranking method; – indicating that feature selection algorithm is not used. The form of the dataset is Recording \(\times\) Segmentation \(\times\) Time. In other papers,
* means that the model was used for distinguishing the different severity levels of congestive heart failure (CHF) disease; the unmarked papers indicated that models were used for diagnosing CHF disease. KNN: k-nearest-neighbor; DT: decision tree; SVM: support vector machine; SPS: sequence forward selection; CART: classification and regression tree; CNN: convolutional neural network; DT-SVM: decision tree based support vector machine.  
\end{table}
Table 11: Comparison between related previous work and our work.  
to diagnosing four severity levels of CHF diseases (four-class classification). Model A applied the SVM classifier and five HRV indicators (TOTPWR, PLF/PHF, SD1, \(\alpha_{2}\), and SampEn) with an accuracy of 97.35%. Model B applied the KNN (\(K=1\)) classifier and four HRV indicators (PNN20, PNN50, TOTPWR, and FuzzyMEn) with an accuracy of 87.80%. The advantage of model A and B was the use of a small amount of features while ensuring that the model has high accuracy. This advantage indicated the application value of our models in clinical practice.  
Conceptualization, Z.H. and W.W.; Methodology, Z.H.; Validation, Z.H. and W.W.; Formal analysis, C.C. and R.Z.; Data curation, C.C. and R.Z.; Writing--original draft preparation, Z.H.; writing--Review and editing, G.L. and W.W.  
This research was funded by the National Science Foundation of China (Grant No. 61103132 and No. 61872301) and the Fundamental Research Funds for the Central Universities of China (Grant No. XDJK2013A020).  
The authors would like to thank all the participants taken part in the experiments.  
The authors declare no conflict of interest.