# Improving Accuracy of Heart Failure Detection Using Data Refinement

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Physical Threshold-based SampEn

^{−1}times the number of ${X}_{j}^{m}$ (1 ≤ j ≤ N − m) that meets ${d}_{i,j}^{m}$ ≤ r. Similarly, set ${A}_{i}^{m}$(r) as (N−m)

^{−1}times the number of ${X}_{j}^{m+1}$ that meets ${d}_{i,j}^{m+1}$ ≤ r for all 1 ≤ j ≤ N − m. Typically, recommended r for clinical use is between 0.10 and 0.25 times the standard deviation (SD) of the data [31]. Nevertheless, under certain circumstances, NSR group presented higher SampEn results than those in CHF group when r was set to 0.10, while the outcomes reversed as r increased to 0.25 [32]. The inverted entropy results make it hard to establish a unified standard to detect CHF subjects with a constant r value. To avoid such inconsistency, we proposed a physical threshold as multiple of sampling period, and proved that it is more adaptive to CHF detection than the traditional threshold [29]. Since the signals were sampled at 128 Hz, we regarded sampling period as 8 ms, and set threshold r as 1.5 times the sampling period, which equals to 12 ms.

^{m}to 10

^{m}

^{+1}.

#### 2.2. Why SampEn Results of Heart Failure Individuals Resemble Normal Ones

#### 2.3. Discrimination of fast HR Sequences

#### 2.4. Dynamic Time Warping

- ⬤
- It starts and finishes in diagonally opposite corner sides of the matrix.
- ⬤
- The cells in the warping path have to be adjacent (including diagonally).
- ⬤
- The points in W have to be placed monotonically in space.

## 3. Data and Experiment

#### 3.1. Data

#### 3.2. Experiment Scheme

#### 3.3. Statistical Analysis

- Sensitivity: $\mathrm{Se}=\mathrm{TP}/\left(\mathrm{TP}+\mathrm{FN}\right)$
- Specificity: $\mathrm{Sp}=\mathrm{TN}/\left(\mathrm{TN}+\mathrm{FP}\right)$
- Accuracy: $\mathrm{Acc}=\left(\mathrm{TP}+\mathrm{TN}\right)/\left(\mathrm{TP}+\mathrm{FP}+\mathrm{FN}+\mathrm{TN}\right)$

- $\mathrm{J}=\underset{c}{\mathrm{max}}\left\{Se\left(c\right)+Sp\left(c\right)-1\right\}$

## 4. Results

#### 4.1. Results of Fast HR Sequences Selection and DTW Processing

#### 4.2. Results of Subject Filtering

## 5. Discussion

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Aljaaf, A.J.; Al-Jumeily, D.; Hussain, A.J.; Dawson, T.; Al-Jumaily, M. Predicting the likelihood of heart failure with a multi level risk assessment using decision tree. In Proceedings of the Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), Beirut, Lebanon, 29 April–1 May 2015; pp. 101–106. [Google Scholar]
- Ward, C. Introduction. In A Practical Guide to Heart Failure in Older People; Ward, C., Witham, M., Eds.; John Wiley & Sons: Chichester, UK, 2009; pp. 1–7. [Google Scholar]
- Roger, V.L. The heart failure epidemic. Int. J. Environ. Res. Public Health
**2010**, 7, 1807–1830. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bhaduri, A.; Bhaduri, S.; Ghosh, D. Visibility graph analysis of heart rate time series and bio-marker of congestive heart failure. Phys. A
**2017**, 482, 786–795. [Google Scholar] [CrossRef] - Ponikowski, P.; Anker, S.D.; AlHabib, K.F.; Cowie, M.R.; Force, T.L.; Hu, S.; Jaarsma, T.; Krum, H.; Rastogi, V.; Rohde, L.E. Heart failure: Preventing disease and death worldwide. ESC Heart Fail.
**2014**, 1, 4–25. [Google Scholar] [CrossRef] [PubMed] - Ambrosy, A.P.; Fonarow, G.C.; Butler, J.; Chioncel, O.; Gheorghiade, M. The global health and economic burden of hospitalizations for heart failure: Lessons learned from hospitalized heart failure registries. J. Am. Coll. Cardiol.
**2014**, 63, 1123–1133. [Google Scholar] [CrossRef] - Blumenfeld, J.D.; Laragh, J.H. Diagnosis and management of heart failure. BMJ
**1994**, 308, 321–328. [Google Scholar] - Hunt, S. Acc/aha 2005 guideline update for the diagnosis and management of chronic heart failure in the adult: A report of the american college of cardiology/american heart association task force on practice guidelines (writing committee to update the 2001 guidelines for the evaluation and management of heart failure). J. Am. Coll. Cardiol.
**2005**, 46, e1–e82. [Google Scholar] [PubMed] [Green Version] - Fuat, A.; Hungin, A.; Murphy, J.J. Barriers to accurate diagnosis and effective management of heart failure in primary care: Qualitative study. BMJ
**2003**, 326, 196. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Fonseca, C.; Mota, T.; Morais, H.; Matias, F.; Costa, C.; Oliveira, A.G.; Ceia, F. The value of the electrocardiogram and chest x-ray for confirming or refuting a suspected diagnosis of heart failure in the community. Eur. J. Heart Fail.
**2004**, 6, 807–812. [Google Scholar] [CrossRef] - Mccraty, R.; Shaffer, F. Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Glob. Adv. Health Med.
**2015**, 4, 46–61. [Google Scholar] [CrossRef] [Green Version] - Shaffer, F.; Ginsberg, J.P. An overview of heart rate variability metrics and norms. Front. Public Health
**2017**, 5, 258. [Google Scholar] [CrossRef] [Green Version] - Catai, A.M.; Pastre, C.M.; Godoy, M.F.; Silva, E.; Takahashi, A.C.M.; Vanderlei, L.C.M. Heart rate variability: Are you using it properly? Standardisation checklist of procedures. Braz. J. Phys. Ther.
**2020**, 24, 91–102. [Google Scholar] [CrossRef] [PubMed] - Goldberger, A.L. Is the normal heartbeat chaotic or homeostatic? News Physiol. Sci.
**1991**, 6, 87–91. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Malik, M. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation
**1996**, 93, 1043–1065. [Google Scholar] [CrossRef] [Green Version] - Pincus, S.M.; Goldberger, A.L. Physiological time-series analysis: What does regularity quantify? Am. J. Physiol. Heart Circ. Physiol.
**1994**, 266, H1643–H1656. [Google Scholar] [CrossRef] - Richman, J.S.; Moorman, J.R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol.
**2000**, 278, H2039–H2049. [Google Scholar] [CrossRef] [Green Version] - Ho, K.K.; Moody, G.B.; Peng, C.K.; Mietus, J.E.; Larson, M.G.; Levy, D.; Goldberger, A.L. Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation
**1997**, 96, 842. [Google Scholar] [CrossRef] - Liu, C.; Murray, A. Applications of complexity analysis in clinical heart failure. In Complexity and Nonlinearity in Cardiovascular Signals; Barbieri, R., Scilingo, E.P., Valenza, G., Eds.; Springer International Publishing: Cham, Germany, 2017; pp. 301–325. [Google Scholar]
- Aktaruzzaman, M.; Sassi, R. Parametric estimation of sample entropy in heart rate variability analysis. Biomed. Signal Process. Control
**2014**, 14, 141–147. [Google Scholar] [CrossRef] - Liu, G.; Wang, L.; Wang, Q.; Zhou, G.M.; Wang, Y.; Jiang, Q. A new approach to detect congestive heart failure using short-term heart rate variability measures. PLoS ONE
**2014**, 9, e93399. [Google Scholar] [CrossRef] - Jovic, A.; Brkic, K.; Krstacic, G. Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach. Biomed. Signal Process. Control
**2019**, 53, 101583. [Google Scholar] [CrossRef] - Hu, B.; Wei, S.; Wei, D.; Zhao, L.; Zhu, G.; Liu, C. Multiple time scales analysis for identifying congestive heart failure based on heart rate variability. IEEE Access
**2019**, 7, 17862–17871. [Google Scholar] [CrossRef] - Isler, Y.; Narin, A.; Ozer, M.; Perc, M. Multi-stage classification of congestive heart failure based on short-term heart rate variability. Chaos Solitons Fractals
**2019**, 118, 145–151. [Google Scholar] [CrossRef] - Melillo, P.; Fusco, R.; Sansone, M.; Bracale, M.; Pecchia, L. Discrimination power of long-term heart rate variability measures for chronic heart failure detection. Med. Biol. Eng. Comput.
**2011**, 49, 67–74. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Masetic, Z.; Subasi, A. Congestive heart failure detection using random forest classifier. Comput. Methods Programs Biomed.
**2016**, 130, 54–64. [Google Scholar] [CrossRef] [PubMed] - Perkiömäki, J.S.; Zareba, W.; Kalaria, V.G.; Couderc, J.P.; Moss, A.J. Comparability of nonlinear measures of hrv between long- and short-term electrocardiographic recordings. Am. J. Cardiol.
**2001**, 87, 905–908. [Google Scholar] [CrossRef] - Maestri, R.; Pinna, G.D.; Accardo, A.; Allegrini, P.; Balocchi, R.; D’Addio, G.; Ferrario, M.; Menicucci, D.; Porta, A.; Sassi, R.; et al. Nonlinear indices of heart rate variability in chronic heart failure patients: Redundancy and comparative clinical value. J. Cardiovasc. Electrophysiol.
**2007**, 18, 425–433. [Google Scholar] [CrossRef] - Xiong, J.; Liang, X.; Zhu, T.; Zhao, L.; Li, J.; Liu, C. A new physically meaningful threshold of sample entropy for detecting cardiovascular diseases. Entropy
**2019**, 21, 830. [Google Scholar] [CrossRef] [Green Version] - Zhang, T.; Yang, Z.; Coote, J.H. Cross-sample entropy statistic as a measure of complexity and regularity of renal sympathetic nerve activity in the rat. Exp. Physiol.
**2007**, 92, 659–669. [Google Scholar] [CrossRef] - Pincus, S.M. Assessing serial irregularity and its implications for health. Ann. N. Y. Acad. Sci.
**2001**, 954, 245–267. [Google Scholar] [CrossRef] - Zhao, L.N.; Wei, S.S.; Zhang, C.Q.; Zhang, Y.T.; Jiang, X.E.; Liu, F.; Liu, C.Y. Determination of sample entropy and fuzzy measure entropy parameters for distinguishing congestive heart failure from normal sinus rhythm subjects. Entropy
**2015**, 17, 6270–6288. [Google Scholar] [CrossRef] [Green Version] - Pincus, S.M.; Huang, W.M. Approximate entropy: Statistical properties and applications. Commun. Stat. Theory Methods
**1992**, 21, 3061–3077. [Google Scholar] [CrossRef] - Bjerregaard, P. Mean 24 hour heart rate, minimal heart rate and pauses in healthy subjects 40–79 years of age. Eur. Heart J.
**1983**, 4, 44–51. [Google Scholar] [CrossRef] [PubMed] - Bennett, J.A.; Barbara, R.; Vera, B.; Joyce, N. Validity and reliability of the nyha classes for measuring research outcomes in patients with cardiac disease. Heart Lung J. Acute Crit. Care
**2002**, 31, 262–270. [Google Scholar] [CrossRef] [PubMed] - Dávila, D.F.; Núñez, T.J.; Odreman, R.; de Dávila, C.A.M. Mechanisms of neurohormonal activation in chronic congestive heart failure: Pathophysiology and therapeutic implications. Int. J. Cardiol.
**2005**, 101, 343–346. [Google Scholar] [CrossRef] [PubMed] - Fan, X.J.; Ren, J. Compensation: A contemporary regulatory machinery in cardiovascular diseases? Cardiovasc. Toxicol.
**2012**, 12, 275–284. [Google Scholar] [CrossRef] [PubMed] - Malliani, A.; Pagani, M. The role of the sympathetic nervous system in congestive heart failure. Eur. Heart J.
**1983**, 4, 49–54. [Google Scholar] [CrossRef] [PubMed] - Sousa-Pinto, B.; Ferreira-Pinto, M.J.; Santos, M.; Leite-Moreira, A.F. Central nervous system circuits modified in heart failure: Pathophysiology and therapeutic implications. Heart Fail. Rev.
**2014**, 19, 759–779. [Google Scholar] - Kamen, P.W.; Krum, H.; Tonkin, A.M. Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clin. Sci.
**1996**, 91, 201–208. [Google Scholar] [CrossRef] [Green Version] - Binkley, P.F.; Enrico, N.; Garrie, J.H.; Steven, D.N.; Robert, J.C. Parasympathetic withdrawal is an integral component of autonomic imbalance in congestive heart failure: Demonstration in human subjects and verification in a paced canine model of ventricular failure. J. Am. Coll. Cardiol.
**1991**, 18, 464–472. [Google Scholar] [CrossRef] [Green Version] - Pierpont, M.E.M.; Foker, J.E.; Pierpont, G.L. Myocardial carnitine metabolism in congestive heart failure induced by incessant tachycardia. Basic Res. Cardiol.
**1993**, 88, 362–370. [Google Scholar] - Mitsa, T. Temporal data similarity computation, representation, and summarization. In Temporal Data Mining; Kumar, V., Ed.; Chapman & Hall/CRC: Boca Raton, FL, USA, 2010; pp. 28–31. [Google Scholar]
- Goldberger, A.L.; Amaral, L.A.; Glass, L.; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.K.; Stanley, H.E. Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation
**2000**, 101, 215–220. [Google Scholar] [CrossRef] [Green Version] - Liu, C.Y.; Li, L.P.; Zhao, L.N.; Zheng, D.C.; Li, P.; Liu, C.C. A combination method of improved impulse rejection filter and template matching for identification of anomalous intervals in electrocardiographic rr sequences. J. Med. Biol. Eng.
**2012**, 32, 245–250. [Google Scholar] [CrossRef] - Zhao, L.; Liu, C.; Wei, S.; Shen, Q.; Zhou, F.; Li, J. A new entropy-based atrial fibrillation detection method for scanning wearable ecg recordings. Entropy
**2018**, 20, 904. [Google Scholar] [CrossRef] [Green Version] - Tripoliti, E.E.; Papadopoulos, T.G.; Karanasiou, G.S.; Naka, K.K.; Fotiadis, D.I. Heart failure: Diagnosis, severity estimation and prediction of adverse events through machine learning techniques. Comput. Struct. Biotechnol. J.
**2016**, 15, 26–47. [Google Scholar] [CrossRef] [Green Version] - Qu, Z.; Liu, Q.; Liu, C. Classification of congestive heart failure with different new york heart association functional classes based on heart rate variability indices and machine learning. Expert Syst.
**2019**, 36, e12396. [Google Scholar] [CrossRef] - Alvarez-Ramirez, J.; Eduardo, R.; Echeverria, J.C.; Luca, A.; Alejandra, V. Heart beat dynamics during sleep and wake phases: A feedback control approach. Phys. A
**2005**, 348, 281–303. [Google Scholar] [CrossRef] - Carroll, D.; McGovern, M. Cardiac, respiratory and metabolic changes during static exercise and voluntary heart rate acceleration. Biol. Psychol.
**1983**, 17, 121–130. [Google Scholar] [CrossRef] - Ricca-Mallada, R.; Migliaro, E.R.; Piskorski, J.; Guzik, P. Exercise training slows down heart rate and improves deceleration and acceleration capacity in patients with heart failure. J. Electrocardiol.
**2012**, 45, 214–219. [Google Scholar] [CrossRef] - Yang, G.; Ren, Y.; Pan, Q.; Ning, G.; Gong, S.; Cai, G.; Zhang, Z.; Li, L.; Yan, J. A heart failure diagnosis model based on support vector machine. In Proceedings of the 2010 3rd International Conference on Biomedical Engineering and Informatics, Yantai, China, 16–18 October 2010; pp. 1105–1108. [Google Scholar]

**Figure 1.**The average SampEn values when using m = 1, r = 12 ms and N = 300 for (

**A**) NSR subjects and (

**B**) CHF subjects.

**Figure 2.**The SampEn distribution of all RR segments when m = 1, r = 12 ms and N = 300 for (

**A**) NSR group and (

**B**) CHF group.

**Figure 3.**The distribution ranges of SD values using N = 300 for (

**A1**) NSR001, (

**A2**) NSR002, (

**B1**) CHF205 and (

**B2**) CHF202.

**Figure 4.**Selection of fast HR sequence with length N = 1000 and 300 respectively for (

**A**) NSR001 and (

**B**) CHF205.

**Figure 6.**Block diagram of the proposed procedure. NSR: normal sinus rhythm, CHF: congestive heart failure, SD: standard deviation, DTW: dynamic time warping. Herein, segments imply RR segments of length 300.

**Figure 7.**The SampEn distribution of RR segments involved in calculation when m = 1, r = 12 ms and N = 300 for (

**A**) Datast0 and (

**B**) Dataset1.

**Figure 8.**Distribution ranges of SampEn between NSR and CHF groups when m = 1, r = 12 ms and N = 300 for Datast0 and Dataset1. *’means there is a significant difference between NSR and CHF groups.

**Figure 9.**ROC curve plots with AUC values in the RR Interval Databases for classifying NSR and CHF subjects. SampEn results using Dataset0, Dataset1 and Dataset2 are presented respectively. Parameter combination of m = 1, r = 12 ms and N = 300 was used.

**Figure 10.**The SampEn distribution of RR segments involved in calculation before and after DTW processing when m = 1, r = 12 ms and N = 300. The top two sub-figures (

**A1**,

**A2**) show type I subjects and type II subjects respectively before DTW. The bottom two sub-figures (

**B1**,

**B2**) show type I subjects and type II subjects respectively after DTW.

**Figure 11.**Examples of RR segments removed and preserved after DTW processing for NSR005 and CHF224. The top two sub-figures (

**A1**,

**A2**) show two segments from NSR005 removed and preserved respectively after DTW, while bottom sub-figures (

**B1**,

**B2**) show two segments from CHF224 removed and preserved respectively after DTW. Herein, segment length N was fixed as 300.

**Figure 12.**Results of SampEn before and after DTW processing for NSR and CHF groups using m = 1 and 2, r = 12 ms, and N = 300. Herein, subjects are divided into two types: type I in (

**A**) with segments no less than 90, and type II in (

**B**) with segments less than 90. The symbol ‘*’ means statistical significance p < 0.01.

**Figure 13.**ROC curve plots with AUC values of type I subjects for heart failure detection before and after DTW processing. Herein, embedding dimension m was set as 1, physical threshold r = 12 ms, and segment length N = 300.

**Figure 14.**(

**A**) Distribution ranges of SampEn with traditional threshold between NSR and CHF groups for Datast0 and type I after DTW. ‘*’means there is a significant difference between NSR and CHF groups. (

**B**) ROC curve plots with AUC values of Dataset0 and type I subjects after DTW processing for heart failure detection. Herein, embedding dimension m was set as 1, traditional threshold r = 0.10, and segment length N = 300.

**Table 1.**Statistical results of the numbers of RR interval recordings, RR intervals and RR segments from the 54 NSR and 29 CHF RR Interval Databases for original dataset, dataset with fast HR sequence selection, dataset after DTW calculation as well as subject extraction.

Variables | NSR Group | CHF Group |
---|---|---|

No. of RR interval recordings | 54 | 29 |

No. of RR intervals | 5,790,504 | 3,312,195 |

No. of RR intervals after removing greater than 2 s | 5,780,148 | 3,306,394 |

No. of RR intervals after removing abnormal heartbeats | 5,738,937 | 3,102,120 |

No. of RR segments (N = 300) after removing abnormal heartbeats | 19,101 | 10,324 |

No. of RR intervals after fast HR sequence selection | 632,100 | 775,200 |

No. of RR segments (N = 300) after fast HR sequence selection | 2107 | 2584 |

No. of RR segments (N = 300) after DTW calculation | 1718 | 2411 |

No. of RR segments (N = 300) for type I subjects | 625 | 1921 |

No. of RR segments (N = 300) for type II subjects | 1093 | 490 |

**Table 2.**Results of SampEn from the parameter combination of embedding dimension m = 1 and 2, physical threshold r = 12 ms, and segment length N = 300. Herein, three datasets are used: Dataset0 with all RR segments preserved, Dataset1 with only fast HR sequences preserved, and Dataset2 with DTW processing added. P-value measured the statistical significance between the NSR and CHF groups at each combination of (m, r). Data are expressed as number or mean ± standard deviation (SD). ‘*’: statistical significance P < 0.01.

SampEn of Dataset0 | SampEn of Dataset1 | SampEn of Dataset2 | |||||||
---|---|---|---|---|---|---|---|---|---|

NSR | CHF | p-Value | NSR | CHF | p-Value | NSR | CHF | p-Value | |

m = 1 | 1.06 ± 0.22 | 0.72 ± 0.28 | 7 × 10^{−8} * | 0.70 ± 0.13 | 0.46 ± 0.11 | 8 × 10^{−321} * | 0.72 ± 0.12 | 0.46 ± 0.10 | 2 × 10^{−319} * |

m = 2 | 0.97 ± 0.21 | 0.63 ± 0.28 | 2 × 10^{−8} * | 0.59 ± 0.12 | 0.38 ± 0.09 | 3 × 10^{−297} * | 0.61 ± 0.11 | 0.38 ± 0.09 | 8 × 10^{−302} * |

**Table 3.**Sensitivity, specificity and accuracy results of SampEn using Dataset0, Dataset1 and Dataset2 respectively. In the parameter combination, embedding dimension m was set as 1, physical threshold r = 12 ms, and segment length N = 300.

Metric | Equally-Weighted Se and Sp | Highly-Weighted Se | Highly-Weighted Sp | ||||||
---|---|---|---|---|---|---|---|---|---|

Dataset0 | Dataset1 | Dataset2 | Dataset0 | Dataset1 | Dataset2 | Dataset0 | Dataset1 | Dataset2 | |

c | 0.72 | 0.57 | 0.59 | 1.86 | 0.93 | 0.92 | 0.40 | 0.26 | 0.26 |

J(%) | 43.35 | 49.14 | 53.83 | 1.80 | 9.71 | 11.17 | 16.86 | 12.36 | 12.59 |

Se(%) | 60.31 | 75.39 | 79.10 | >99.0 | >99.0 | >99.0 | 17.85 | 13.31 | 13.52 |

Sp(%) | 83.05 | 73.75 | 74.74 | 2.80 | 10.68 | 12.17 | >99.0 | >99.0 | >99.0 |

Acc(%) | 75.07 | 74.65 | 77.28 | 36.55 | 59.35 | 62.87 | 70.53 | 51.82 | 49.12 |

**Table 4.**Sensitivity, specificity and accuracy results of SampEn before and after DTW processing. Subjects with RR segment number $\ge $90 (type I) and RR segment number <90 (type IIII) are set as contrast here. In the parameter combination, embedding dimension m was set as 1, physical threshold r = 12 ms, and segment length N = 300.

Metric | Equally-Weighted Se and Sp | Highly-Weighted Se | Highly-Weighted Sp | |||
---|---|---|---|---|---|---|

Before DTW | After DTW | Before DTW | After DTW | Before DTW | After DTW | |

type I | ||||||

c | 0.59 | 0.61 | 0.74 | 0.74 | 0.35 | 0.48 |

J(%) | 66.49 | 78.93 | 46.66 | 58.05 | 32.00 | 60.93 |

Se(%) | 88.26 | 91.41 | >99.0 | >99.0 | 32.96 | 61.89 |

Sp(%) | 78.24 | 87.52 | 47.65 | 59.04 | >99.0 | >99.0 |

Acc(%) | 84.85 | 90.46 | 81.56 | 89.20 | 55.41 | 71.01 |

type II | ||||||

c | 0.41 | 0.41 | 1.16 | 1.18 | 0.24 | 0.24 |

J(%) | 10.24 | 12.72 | 3.05 | 2.84 | 2.30 | 2.35 |

Se(%) | 20.86 | 21.22 | >99.0 | >99.0 | 3.24 | 3.27 |

Sp(%) | 89.38 | 91.49 | 3.95 | 3.66 | >99.0 | >99.0 |

Acc(%) | 65.86 | 69.74 | 36.60 | 33.23 | 66.17 | 69.43 |

**Table 5.**Sensitivity, specificity and accuracy results of SampEn with traditional threshold before and after data refinement processing. Dataset0 refers to original RR interval databases, and type I after DTW refers to subjects with RR segment number ≥90 after both fast HR sequence selection and DTW calculation. In the parameter combination, embedding dimension m was set as 1, traditional threshold r = 0.10, and segment length N = 300.

Metric | Equally-Weighted Se and Sp | Highly-Weighted Se | Highly-Weighted Sp | |||
---|---|---|---|---|---|---|

Dataset0 | Type I after DTW | Dataset0 | Type I after DTW | Dataset0 | Type I after DTW | |

c | 0.68 | 1.57 | 2.76 | 1.75 | 0.89 | 1.35 |

J(%) | 37.13 | 75.18 | 1.74 | 47.49 | 0.24 | 50.21 |

Se(%) | 60.57 | 90.06 | >99.0 | >99.0 | 1.24 | 51.17 |

Sp(%) | 76.56 | 85.12 | 2.74 | 44.48 | >99.0 | >99.0 |

Acc(%) | 70.95 | 88.85 | 36.51 | 86.61 | 64.70 | 62.92 |

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## Share and Cite

**MDPI and ACS Style**

Xiong, J.; Liang, X.; Zhao, L.; Lo, B.; Li, J.; Liu, C.
Improving Accuracy of Heart Failure Detection Using Data Refinement. *Entropy* **2020**, *22*, 520.
https://doi.org/10.3390/e22050520

**AMA Style**

Xiong J, Liang X, Zhao L, Lo B, Li J, Liu C.
Improving Accuracy of Heart Failure Detection Using Data Refinement. *Entropy*. 2020; 22(5):520.
https://doi.org/10.3390/e22050520

**Chicago/Turabian Style**

Xiong, Jinle, Xueyu Liang, Lina Zhao, Benny Lo, Jianqing Li, and Chengyu Liu.
2020. "Improving Accuracy of Heart Failure Detection Using Data Refinement" *Entropy* 22, no. 5: 520.
https://doi.org/10.3390/e22050520