Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability

Yu, Sung‐Nien; Lee, Ming-Yuan

doi:10.1016/j.compbiomed.2012.06.005

Cited by 79 publications

(58 citation statements)

References 22 publications

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“…Moreover, the patients who died during the follow-up period presented further reduced LF power and steeper 1/f slope than the survivors [19]. Yu and Lee used the bispectral analysis and genetic algorithm (GA) for CHF recognition [10]. Makikallio et al showed that a short-term fractal scaling exponent was the strongest predictor of mortality of CHF [20].…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Entropy Analysis of the Differential RR Interval Time Series Signal and Its Application in Detecting Congestive Heart Failure

Liu

Gao

2017

Entropy

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Cardiovascular systems essentially have multiscale control mechanisms. Multiscale entropy (MSE) analysis permits the dynamic characterization of the cardiovascular time series for both short-term and long-term processes, and thus can be more illuminating. The traditional MSE analysis for heart rate variability (HRV) is performed on the original RR interval time series (named as MSE_RR). In this study, we proposed an MSE analysis for the differential RR interval time series signal, named as MSE_dRR. The motivation of using the differential RR interval time series signal is that this signal has a direct link with the inherent non-linear property of electrical rhythm of the heart. The effectiveness of the MSE_RR and MSE_dRR were tested and compared on the long-term MIT-Boston's Beth Israel Hospital (MIT-BIH) 54 normal sinus rhythm (NSR) and 29 congestive heart failure (CHF) RR interval recordings, aiming to explore which one is better for distinguishing the CHF patients from the NSR subjects. Four RR interval length for analysis were used (N = 500, N = 1000, N = 2000 and N = 5000). The results showed that MSE_RR did not report significant differences between the NSR and CHF groups at several scales for each RR segment length type (Scales 7, 8 and 10 for N = 500, Scales 3 and 10 for N = 1000, Scales 2 and 3 for both N = 2000 and N = 5000). However, the new MSE_dRR gave significant separation for the two groups for all RR segment length types except N = 500 at Scales 9 and 10. The area under curve (AUC) values from the receiver operating characteristic (ROC) curve were used to further quantify the performances. The mean AUC of the new MSE_dRR from Scales 1-10 are 79.5%, 83.1%, 83.5% and 83.1% for N = 500, N = 1000, N = 2000 and N = 5000, respectively, whereas the mean AUC of MSE_RR are only 68.6%, 69.8%, 69.6% and 67.1%, respectively. The five-fold cross validation support vector machine (SVM) classifier reports the classification Accuracy (Acc) of MSE_RR as 73.5%, 75.9% and 74.6% for N = 1000, N = 2000 and N = 5000, respectively, while for the new MSE_dRR analysis accuracy was 85.5%, 85.6% and 85.6%. Different biosignal editing methods (direct deletion and interpolation) did not change the analytical results. In summary, this study demonstrated that compared with MSE_RR, MSE_dRR reports better statistical stability and better discrimination ability for the NSR and CHF groups.

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Section: Introductionmentioning

confidence: 99%

“…HRV analysis has given an insight into understanding the abnormalities of CHF, and can be used to identify the higher-risk CHF patients [9][10][11][12][13]. Depressed HRV has been used as a risk predictor in CHF [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Entropy Analysis of the Differential RR Interval Time Series Signal and Its Application in Detecting Congestive Heart Failure

Liu

Gao

2017

Entropy

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“…The Detrended Fluctuation Analysis (DFA)-based features with SVM yielded 96% classification accuracy to discriminate normal and CHF HRV signals in [4]. In [60], the authors have studied time domain features, frequency domain features and bispectrum features to analyze HRV signals of CHF and normal subjects. They incorporated Genetic Algorithm (GA) and SVM classifier in their method and obtained 98.79% classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Kumar

Pachori

Acharya

2017

Entropy

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Abstract:In the present work, an automated method to diagnose Congestive Heart Failure (CHF) using Heart Rate Variability (HRV) signals is proposed. This method is based on Flexible Analytic Wavelet Transform (FAWT), which decomposes the HRV signals into different sub-band signals. Further, Accumulated Fuzzy Entropy (AFEnt) and Accumulated Permutation Entropy (APEnt) are computed over cumulative sums of these sub-band signals. This provides complexity analysis using fuzzy and permutation entropies at different frequency scales. We have extracted 20 features from these signals obtained at different frequency scales of HRV signals. The Bhattacharyya ranking method is used to rank the extracted features from the HRV signals of three different lengths (500, 1000 and 2000 samples). These ranked features are fed to the Least Squares Support Vector Machine (LS-SVM) classifier. Our proposed system has obtained a sensitivity of 98.07%, specificity of 98.33% and accuracy of 98.21% for the 500-sample length of HRV signals. Our system yielded a sensitivity of 97.95%, specificity of 98.07% and accuracy of 98.01% for HRV signals of a length of 1000 samples and a sensitivity of 97.76%, specificity of 97.67% and accuracy of 97.71% for signals corresponding to the 2000-sample length of HRV signals. Our automated system can aid clinicians in the accurate detection of CHF using HRV signals. It can be installed in hospitals, polyclinics and remote villages where there is no access to cardiologists.

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“…They also reported an increased specificity of 89.7 % by taking two non-standard long-term HRV measures into consideration. Sung-Nien Yu and Ming-Yuan Lee [23,24] proposed two different classification algorithms to recognize CHF patients using HRV based on feature selection. One algorithm uses the conditional mutual information feature selector with uniform distribution (UCMIFS) and the other uses a GA feature selector.…”

Section: Introductionmentioning

confidence: 99%

“…This can be only obtained by studying such systems on multiple time scales. In a recent study, Multiscale Entropy (MSE) analysis approach [10,11] has been applied to 24 hour ECG recordings to analyze heart rate dynamics.…”

Section: Introductionmentioning

confidence: 99%

Reduced Data Dualscale Entropy Analysis of HRV Signals for Improved Congestive Heart Failure Detection

Kuntamalla¹,

Lekkala²

2014

Measurement Science Review

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Heart rate variability (HRV) is an important dynamic variable of the cardiovascular system, which operates on multiple time scales. In this study, Multiscale entropy (MSE) analysis is applied to HRV signals taken from Physiobank to discriminate Congestive Heart Failure (CHF) patients from healthy young and elderly subjects. The discrimination power of the MSE method is decreased as the amount of the data reduces and the lowest amount of the data at which there is a clear discrimination between CHF and normal subjects is found to be 4000 samples. Further, this method failed to discriminate CHF from healthy elderly subjects. In view of this, the Reduced Data Dualscale Entropy Analysis method is proposed to reduce the data size required (as low as 500 samples) for clearly discriminating the CHF patients from young and elderly subjects with only two scales. Further, an easy to interpret index is derived using this new approach for the diagnosis of CHF. This index shows 100 % accuracy and correlates well with the pathophysiology of heart failure.

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Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability

Cited by 79 publications

References 22 publications

Multiscale Entropy Analysis of the Differential RR Interval Time Series Signal and Its Application in Detecting Congestive Heart Failure

Multiscale Entropy Analysis of the Differential RR Interval Time Series Signal and Its Application in Detecting Congestive Heart Failure

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Reduced Data Dualscale Entropy Analysis of HRV Signals for Improved Congestive Heart Failure Detection

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