Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects

Wang, Yan; Wei, Shoushui; Zhang, Shuai; Zhang, Yatao; Zhao, Lina; Liu, Chengyu; Murray, Alan

doi:10.1016/j.bspc.2018.01.001

Cited by 40 publications

(30 citation statements)

References 40 publications

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“…İşler et al reported that the combination of the classical HRV indices with indices obtained from wavelet entropy calculations could significantly improve the performance of the HRV analysis in CHF screening [ 32 ]. In this paper, the LF/HF ratio is significantly distinguished between the normal group and CHF group ( Figure 5 ), which is consistent with other findings studying HRV features of CHF [ 33 , 34 , 35 , 36 ]. However, they only focused on decomposing independent frequency components of HRV but neglected a complex interaction between the independent frequency components of HRV.…”

Section: Discussionsupporting

confidence: 92%

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

Luo

Pan

et al. 2018

Entropy

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Congestive heart failure (CHF) is a cardiovascular disease associated with autonomic dysfunction, where sympathovagal imbalance was reported in many studies using heart rate variability (HRV). To learn more about the dynamic interaction in the autonomic nervous system (ANS), we explored the directed interaction between the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) with the help of transfer entropy (TE). This article included 24-h RR interval signals of 54 healthy subjects (31 males and 23 females, 61.38 ± 11.63 years old) and 44 CHF subjects (8 males and 2 females, 19 subjects’ gender were unknown, 55.51 ± 11.44 years old, 4 in class I, 8 in class II and 32 in class III~IV, according to the New York Heart Association Function Classification), obtained from the PhysioNet database and then segmented into 5-min non-overlapping epochs using cubic spline interpolation. For each segment in the normal group and CHF group, frequency-domain features included low-frequency (LF) power, high-frequency (HF) power and LF/HF ratio were extracted as classical estimators of autonomic activity. In the nonlinear domain, TE between LF and HF were calculated to quantify the information exchanging between SNS and PNS. Compared with the normal group, an extreme decrease in LF/HF ratio (p = 0.000) and extreme increases in both TE(LF→HF) (p = 0.000) and TE(HF→LF) (p = 0.000) in the CHF group were observed. Moreover, both in normal and CHF groups, TE(LF→HF) was a lot greater than TE(HF→LF) (p = 0.000), revealing that TE was able to distinguish the difference in the amount of directed information transfer among ANS. Extracted features were further applied in discriminating CHF using IBM SPSS Statistics discriminant analysis. The combination of the LF/HF ratio, TE(LF→HF) and TE(HF→LF) reached the highest screening accuracy (83.7%). Our results suggested that TE could serve as a complement to traditional index LF/HF in CHF screening.

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Section: Discussionsupporting

confidence: 92%

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

Luo

Pan

et al. 2018

Entropy

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“…Sample entropy (SampEn) and fuzzy measure entropy (FuzzyMEn) are often used as measures for the analysis of the complexity of HRV signals [14][15][16][17]. Usually, SampEn and FuzzyMEn are influenced by the parameters of embedding dimension m and tolerance threshold r [16,17]. In this paper, the best combination of m and r for SampEn and FuzzyMEn were obtained by statistical significance analysis.…”

Section: Non-linear Hrv Featuresmentioning

confidence: 99%

“…The linear features are the signal parameters in time-domain, frequency-domain, and time-frequency domain [7][8][9]. In order to reveal other valuable information contained in the IBI signal, some researchers have used the complexity analysis method to extract nonlinear indicators [10][11][12][13][14][15][16][17][18][19][20]. In 2003, Asyali et al [21] applied Bayesian classifiers and nine long-term measurements for CHF discrimination with an accuracy of 93.24%.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Power spectral density (PSD) is nothing but the power of a signal is distributed over frequency (or) can be defined as distribution of power into frequency components forming that as signal. PSD is a type of random signal which is independent of time [2,12]. Power spectral density is used differentiate the frequency component variations in the three different ECG signals in which one is Normal ECG and the other two are ECG of abnormal conditions of the heart.…”

Section: Power Spectral Density (Psd)mentioning

confidence: 99%

Comparison of Normal and Abnormal Conditions in ECG Using RR Variablity and Spectral Density

Thamizhvani¹,

Joseph²,

Babu³

et al. 2018

IJET

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Abnormality of the heart is monitored by Electrocardiograph (ECG). The ECG waveform is formed of PQRS pattern. Differentiation of the abnormalities based on the ECG signal is simple algorithm for diagnosis. ECG data of normal, atrial fibrillation and congestive heart failure is obtained from a authorized database. R peak from the QRS complex is detected using Pan-Tompkins algorithm for analysis. Mean RR and heart rate variability parameters are extracted from the QRS complex detected. With these results, the difference in the three ECG signals can be determined. For further detailed comparison, frequency component variation is analysed using power spectral density. Based on density spectrum, the differentiation of normal and abnormal ECG signals can be determined.

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Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects

Abstract: Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects.

Cited by 40 publications

References 40 publications

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

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

Comparison of Normal and Abnormal Conditions in ECG Using RR Variablity and Spectral Density

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