Linear-nonlinear heart rate variability analysis and SVM based classification of normal and hypertensive subjects

Poddar, M.G.; Kumar, Vinod; Sharma, Yash Paul

doi:10.1016/j.jelectrocard.2013.05.090

Cited by 12 publications

(3 citation statements)

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“…Finally, L 1 -regularized logistic regression and SVM were utilized to distinguish hypertensive patients and normotensive subjects. Poddar et al [20] extracted a lot of HRV-related features from RR interval sequences and then directly fed them into a SVM to identify hypertensive subjects. In order to characterize hypertension pattern more comprehensively, Ghosh et al [7] extracted features from multiple physiological signals and then modeled them by separately employing five common classifiers.…”

Section: Related Workmentioning

confidence: 99%

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

Liu

Zhou

Wang

et al. 2019

Sensors

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Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

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Section: Related Workmentioning

confidence: 99%

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

Liu

Zhou

Wang

et al. 2019

Sensors

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show abstract

“…Many studies diagnose hypertension by extracting features from an ECG signal and directly inputting them into a common machine learning algorithm, such as SVM, logistic regression, Decision Tree, and so on. For example, Poddar et al [19] extracted many HRV-related features from RR intervals sequence based on ECG signal and then fed them into a SVM to identify hypertensive subjects. Similarly, Ni et al [20] employed a logistic regression algorithm to discriminate hypertensive and normotensive.…”

Section: Introductionmentioning

confidence: 99%

C-MWCAR: Classification Based on Multiple Weighted Class Association Rules

Gui

Liu

et al. 2023

Applied Sciences

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Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules (C-MWCAR), whose key idea is to transform the association among features into a set of class association rules (CARs), then classify unknown instances based on the CARs obtained. Concretely, C-MWCAR consists of a dictionary order-based CAR mining algorithm (DOCMA), a branch-based CAR selection algorithm (BCSA), and a multiple weighted CARs-based classifier (MWCC). Specifically, DOCMA mines the complete set of CARs, from which BCSA further selects a representative and concise set of CARs based on the distribution, coverage, and redundancy of the mined CARs. When classifying an unknown instance, MWCC picks out a set of CARs that are most similar to the given instance and computes the weighted importance of those CARs. Finally, the class label of the given instance will be determined by the similarities between the instance and the CARs and the weighted importance of the CARs. Furthermore, we apply the proposed C-MWCAR to a real-world classification task, i.e., hypertension diagnosis, based on a real dataset of 128 subjects. Experimental results indicate that C-MWCAR outperforms four baseline methods and achieves 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively.

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“…A new machine learning method, namely, support vector machine (SVM), is widely employed in many fields, for instance, handwriting recognition, three-dimension objects recognition, faces recognition, text images recognition, voice recognition, and regression analysis [15][16][17][18][19][20][21][22][23]. The SVM based on statistical learning theory has good fitting ability for complex nonlinear function.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Dry Bulk Freight Index with Improved SVM

Han

Yan

Ning

et al. 2014

Mathematical Problems in Engineering

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An improved SVM model is presented to forecast dry bulk freight index (BDI) in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

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Linear-nonlinear heart rate variability analysis and SVM based classification of normal and hypertensive subjects

Cited by 12 publications

References 0 publications

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

C-MWCAR: Classification Based on Multiple Weighted Class Association Rules

Forecasting Dry Bulk Freight Index with Improved SVM

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