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

Hu, Baiyang; Wei, Shoushui; Wei, Dingwen; Zhao, Lina; Zhu, Guohun; Liu, Chengyu

doi:10.1109/access.2019.2895998

Cited by 29 publications

(13 citation statements)

References 47 publications

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“…In most cases, RR intervals of less than 0.4 s may indicate that an R peak was incorrectly detected within a normal RR interval, while > 2.0 s may indicate that an R peak was missed between two normal RR intervals [10]. Therefore, RR intervals less than 0.4 s or greater than 2.0 s were deleted from the original data.…”

Section: Datasetmentioning

confidence: 99%

“…In this study, nine traditional features including the time, frequency, and nonlinear domains were first calculated [10]. Time domain features include MEAN, SDNN, and RMSSD [13], where the MEAN is the average value of the RR interval, the SDNN was used to evaluate the overall variability of the heart rates, and the RMSSD was used to evaluate the short-term variability of the heart rates.…”

Section: Hrv Featuresmentioning

confidence: 99%

“…Their study suggested that multi-time-scales analysis has a reference value for the diagnosis of heart disease. Hu et al [10] continued and completed the work of Costa et al by selecting the optimal time-scale for the features in the analysis of HRV for the diagnosis of CHF. In addition, new nonstandard features were constructed based on feature variation trends in multi-time-scales analysis to optimize the classification effect of patients with CHF and NSR.…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection Scheme Based on Multi-time-scales for Analyzing Congestive Heart Failure

Wang

Zhang

Jiang

et al. 2021

Preprint

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This paper proposed a feature selection method combined with multi-time-scales analysis and heart rate variability (HRV) analysis for middle and early diagnosis of congestive heart failure (CHF). In previous studies regarding the diagnosis of CHF, researchers have tended to increase the variety of HRV features by searching for new ones or to use different machine learning algorithms to optimize the classification of CHF and normal sinus rhythms subject (NSR). In fact, the full utilization of traditional HRV features can also improve classification accuracy. The proposed method constructs a multi-time-scales feature matrix according to traditional HRV features that exhibit good stability in multiple time-scales and differences in different time-scales. The multi-scales features yield better performance than the traditional single-time-scales features when the features are fed into a support vector machine (SVM) classifier, and the results of the SVM classifier exhibit a sensitivity, a specificity, and an accuracy of 99.52%, 100.00%, and 99.83%, respectively. These results indicate that the proposed feature selection method can effectively reduce redundant features and computational load when used for automatic diagnosis of CHF.

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Section: Datasetmentioning

confidence: 99%

Section: Hrv Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Selection Scheme Based on Multi-time-scales for Analyzing Congestive Heart Failure

Wang

Zhang

Jiang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…In general, most of the literature on using machine learning for heart disease diagnosis utilized two major techniques: the ANN [16,17,18,19] and the SVM [20,21,22,23]. They both have high classification accuracy, but they suffer from low learning speed when the number of instances or the number of features is huge.…”

Section: Dis-cat3mentioning

confidence: 99%

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Meshref¹

2019

IJACSA

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Research on heart diseases has always been the center of attention of the world health organization. More than 17.9 million people died from it in 2016, which represent 31% of the overall deaths globally. Machine learning techniques have been used extensively in that area to assist physicians to develop a firm opinion about the conditions of their heart disease patients. Some of the existing machine learning models still suffers from limited predication ability, and the chosen analysis approaches are not suitable. As well, it was noticed that the existing approaches pay more attention to building high accuracy models, while overlooking the ability to interpret and understand the recommendations of these models. In this research, different renowned machine learning techniques: Artificial Neural Networks, Support Vector Machines, Naïve Bayes, Decision Trees and Random Forests have been investigated to help in building, understanding and interpreting different heart disease diagnosing models. The Artificial Neural Networks model showed the best accuracy of 84.25% compared to the other models. In addition, it was found that despite some designed models have higher accuracies than others, it may be safer to choose a lower accuracy model as a final design of this study. This sacrifice was essential to make sure that a more transparent and trusted model is being used in the heart disease diagnosis process. This transparency validation was conducted using a newly suggested metric: the Feature Ranking Cost index. The use of that index showed promising results by making it clear as which machine learning model has a balance between accuracy and transparency. It is expected that following the detailed analyses and the use of this research findings will be useful to the machine learning community as it could be the basis for post-hoc prediction model interpretation of different clinical data sets.

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“…In [ 20 ], a parametric estimation of SampEn on real RR series from both NSR and CHF subjects was proven to be feasible. SampEn has also been widely chosen as a representative feature in novel CHF detection algorithms based on HRV measures and classifiers such as support vector machine (SVM) [ 21 , 22 , 23 , 24 ], which provide effective and computationally efficient tools to automatically diagnose CHF patients. Though diagnosing heart failure with long-term ECG signals (usually 24 h) may lead to accurate results [ 25 , 26 ], some studies have shown that short-term (usually 5 min) ECG recordings could also provide valuable information [ 27 , 28 ], thus the focus of research has recently switched to low-cost, non-invasive, and lightweight classification methods that are based on short-term HRV analysis [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Accuracy of Heart Failure Detection Using Data Refinement

Xiong

Liang

Zhao

et al. 2020

Entropy

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Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection.

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

Cited by 29 publications

References 47 publications

Feature Selection Scheme Based on Multi-time-scales for Analyzing Congestive Heart Failure

Feature Selection Scheme Based on Multi-time-scales for Analyzing Congestive Heart Failure

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Improving Accuracy of Heart Failure Detection Using Data Refinement

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