Detection of Congestive Heart Failure using Renyi Entropy

Cornforth, David; Jelinek, Herbert F.

doi:10.22489/cinc.2016.196-231

Cited by 13 publications

(16 citation statements)

References 14 publications

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“…Narin et al [14] selected 27 features by backward elimination to SVM classifier and obtained the best performance of sensitivity of 82.75%, specificity of 96.29% and accuracy of 91.56%. Cornforth and Jelinek [5] used only four features of HRV measures on the length of 1000 RR intervals. The KNN classifier achieved a [10] selected 34 features from HRV measures including time-domain measures, frequency-domain measures, Poincare plot, detrended fluctuation analysis (DFA), symbolic dynamic and entropy measures.…”

Section: Discussionmentioning

confidence: 99%

“…European Society of Cardiology the North American Society of Pacing Electrophysiology published standards in HRV analysis in 1996 [4]. In the last two decades, numerous studies have focused on diagnosis purpose with HRV measures, especially in detecting patients with CHF from normal sinus rhythms (NSR) subjects [5]- [16]. Depressed HRV measures such as standard deviation of NN intervals (SDNN) in time domain or low frequency power (LF) in frequency domain has been reported as a risk assessment factor for CHF [17]- [19] and the nonlinear HRV measures including Poincare plot and sample entropy (SampEn, SE) also performed significant roles between the healthy and patients with cardiovascular disease [20], [21].…”

Section: Introductionmentioning

confidence: 99%

“…Narin et al [14] used standard short-term HRV measures and wavelet packet transform in addition to several non-linear parameters as feature vector in five typical classifiers. Cornforth and Jelinek [5] combined time-domain HRV measures of 1000 RR intervals and Renyi entropy exponents to identify CHF patients with five classifiers. Kumar et al [11] computed Accumulated Fuzzy Entropy (AFEnt) and Accumulated Permutation Entropy (APEnt) on three different lengths of HRV signals and the extracted features ranked by a Bhattacharyya method were fed to a Least Squares SVM (LS-SVM) classifier.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability

Wei

et al. 2019

IEEE Access

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It is well known that electrocardiogram heartbeats are substantial for cardiac disease diagnosis. In this paper, the best time scale was investigated to recognize congestive heart failure (CHF) based on heart rate variability (HRV) measures. The classifications were performed on seven different time scales with a support vector machine classifier. Nine HRV measures, including three time-domain measures, three frequency-domain measures, and three nonlinear-domain measures, were taken as feature vectors for classifier on each time scale. A total of 83 subjects with RR intervals were analyzed, of which 54 cases were normal and 29 patients were suffering from CHF in PhysioNet databases. The classifying results using tenfold cross-validation method achieved the best performance of a sensitivity, specificity, and accuracy of 86.7%, 98.3%, and 94.4%, respectively, on the 2-h time scale. Moreover, by introducing only three nonstandard HRV features extracted from the trends of HRV measures on time scales, it achieved a better performance of a sensitivity of 93.3%, specificity of 98.3%, and an accuracy of 96.7%. The impressive performance of discrimination power on the 2-h time scale and the trends of HRV measures on time scales indicate that multiple time scales play significant roles in detecting CHF and can be valuable in expressing useful knowledge in medicine. INDEX TERMS Electrocardiogram (ECG), heart rate variability (HRV), congestive heart failure (CHF), multiple time scales, support vector machine (SVM).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability

Wei

et al. 2019

IEEE Access

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“…Nonlinear phenomena often occur in HRV and are determined by the state of the autonomic nervous system, complex hemodynamic interactions and other physiological effects. In this study, Poincaré plots [19], SampEn [31], Renyi entropy (RyEn) [32] and detrended fluctuation analysis (DFA) [45] were used to extract the nonlinear features.…”

Section: ) Nonlinear Featuresmentioning

confidence: 99%

“…SampEn [31] features notable consistency with known random series and has been widely used in the analysis of physiological time series, especially the HRV analysis. Cornforth and Jelinek [32] demonstrated that RyEn can identify CHF effectively. The RyEn is a generalized measure and includes the Shannon entropy as a special case:…”

Section: ) Nonlinear Featuresmentioning

confidence: 99%

Deep Ensemble Detection of Congestive Heart Failure Using Short-Term RR Intervals

et al. 2019

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Heart rate variability (HRV) is an effective predictor of congestive heart failure (CHF). However, important challenges exist regarding the effective temporal feature extraction and efficient classification using high-dimensional HRV representations. To solve these challenges, an ensemble method for CHF detection using short-term HRV data and deep neural networks was proposed. In this paper, five opensource databases, the BIDMC CHF database (BIDMC-CHF), CHF RR interval database (CHF-RR), MIT-BIH normal sinus rhythm (NSR) database, fantasia database (FD), and NSR RR interval database (NSR-RR), were used. Additionally, three RR segment length types (N = 500, 1000, and 2000) were used to evaluate the proposed method. First, we extracted the expert features of RR intervals (RRIs) and then built a long short-term memory-convolutional neural network-based network to extract deep-learning (DL) features automatically. Finally, an ensemble classifier was used for CHF detection using the above features. With blindfold validation (three CHF subjects and three normal subjects), the proposed method achieved 99.85%, 99.41%, and 99.17% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the BIDMC-CHF, NSR, and FD databases. With blindfold validation (six CHF subjects and six normal subjects), the proposed method achieved 83.84%, 87.54%, and 85.71% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the NSR-RR and CHF-RR ndatabases. Based on feature ranking, the significant effectiveness provided by the DL features has been proven. The results have shown that the deep ensemble method can achieve reliable CHF detection using short-term heart rate signals and enable CHF detection through intelligent hardware. INDEX TERMS Electrocardiography, boosting, artificial intelligence.

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Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Eltrass

Tayel

Ammar

2022

Neural Comput & Applic

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Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.

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Detection of Congestive Heart Failure using Renyi Entropy

Abstract: Abstract

Cited by 13 publications

References 14 publications

Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability

Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability

Deep Ensemble Detection of Congestive Heart Failure Using Short-Term RR Intervals

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

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