Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures

Lee, Heon Gyu; Kim, Wuon-Shik; Noh, Ki Yong; Shin, Jin-Ho; Yun, Unil; Ryu, Keun Ho

doi:10.1007/s10796-009-9155-2

Cited by 16 publications

(6 citation statements)

References 19 publications

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“…We extract linear feature vectors in the time and frequency domain and extract non-linear feature vectors of HRV. The literature on HRV feature vector extraction was described in detail in [15]. …”

Section: Linear and Non-linear Feature Vectors Of Hrvmentioning

confidence: 99%

“…The feature vectors in frequency mode use power spectral density (PSD) analysis and extract seven types of feature vectors as follows [13,15] Diagnostic feature vector employs only three vectors; nLF to reflect sympathetic activity, nHF to show parasympathetic activity and LF/HF ratio to mirror the sympathovagal balance (see Table 2). Table 2.…”

Section: Linear Feature Vectors In Frequency Domainmentioning

confidence: 99%

“…We also used Precision, Recall, F-measure and Accuracy to evaluate the classifiers' performance for analyzing our training sets with imbalanced class distribution. Formal definitions of these measures are given in the equations below [15,26,27]:…”

Section: Classification Based On Multiple Association Rules (Cmar)mentioning

confidence: 99%

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A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

et al. 2016

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Abstract:In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular disease (CVD). For inventing the multiple feature vectors, we extract a candidate feature vector through image processing and measurement of the thickness of carotid intima-media (IMT). As a complementary way, the linear and/or nonlinear feature vectors are also extracted from HRV, a main index for cardiac disorder. The significance of the multiple feature vectors is tested with several machine learning methods, namely Neural Networks, Support Vector Machine (SVM), Classification based on Multiple Association Rule (CMAR), Decision tree induction and Bayesian classifier. As a result, multiple feature vectors extracted from both CAs and HRV (CA+HRV) showed higher accuracy than the separative feature vectors of CAs and HRV. Furthermore, the SVM and CMAR showed about 89.51% and 89.46%, respectively, in terms of diagnosing accuracy rate after evaluating the diagnosis or prediction methods using the finally chosen multiple feature vectors. Therefore, the multiple feature vectors devised in this paper can be effective diagnostic indicators of CVD. In addition, the feature vector analysis and prediction techniques are expected to be helpful tools in the decisions of cardiologists.

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Section: Linear and Non-linear Feature Vectors Of Hrvmentioning

confidence: 99%

Section: Linear Feature Vectors In Frequency Domainmentioning

confidence: 99%

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A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

et al. 2016

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“…In the following HRV analysis methods are discussed shortly. A detailed description of HRV features can be found in [8].…”

Section: A Extracting the Featuresmentioning

confidence: 99%

Real-Time Diagnosis System Using Incremental Emerging Pattern Mining

Park

Lee

Park

2010

2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications

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Currently, as a effort to reduce a rate of death by cardiovascular diseases, a lot of researches have been studied regarding real-time diagnosis system. So, we implement a prototype which is contained of stream data processor and incremental data mining module for automatic diagnosis of cardiovascular diseases. In the prototype, (i)ECG signal data which is transported from body-attached sensor is collected and pre-processed, and (ii)diagnosis features of the bio-signal data are extracted. And the patients are automatically diagnosed using the incremental emerging pattern mining module, then (iii)the diagnosis result is provided for the doctor in charge of the patients via a web application in order to manage the medical history of each patient. So, the prototype is able to diagnose and predict patient state on real-time, automatically.

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“…Both linear and nonlinear methods are used to analyse heart rate in healthy subjects and patients with different pathologies [2][3][4]. The nonlinear methods usually supplement the linear ones [5][6][7][8][9][10][11][12]. Many authors especially claim the prognostic value of nonlinear analysis [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Informative Nature and Nonlinearity of Lagged Poincaré Plots Indices in Analysis of Heart Rate Variability

Койчубеков

Риклефс

Sorokina

et al. 2017

Entropy

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Lagged Poincaré plots have been successful in characterizing abnormal cardiac function. However, the current research practices do not favour any specific lag of Poincaré plots, thus complicating the comparison of results of different researchers in their analysis of heart rate of healthy subjects and patients. We researched the informative nature of lagged Poincaré plots in different states of the autonomic nervous system. It was tested in three models: different age groups, groups with different balance of autonomous regulation, and in hypertensive patients. Correlation analysis shows that for lag l = 6, SD1/SD2 has weak (r = 0.33) correlation with linear parameters of heart rate variability (HRV). For l more than 6 it displays even less correlation with linear parameters, but the changes in SD1/SD2 become statistically insignificant. Secondly, surrogate data tests show that the real SD1/SD2 is statistically different from its surrogate value and the conclusion could be made that the heart rhythm has nonlinear properties. Thirdly, the three models showed that for different functional states of the autonomic nervous system (ANS), SD1/SD2 ratio varied only for lags l = 5 and 6. All of this allow to us to give cautious recommendation to use SD1/SD2 with lags 5 and 6 as a nonlinear characteristic of HRV. The received data could be used as the basis for continuing the research in standardisation of nonlinear analytic methods.

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Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures

Cited by 16 publications

References 19 publications

A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

Real-Time Diagnosis System Using Incremental Emerging Pattern Mining

Informative Nature and Nonlinearity of Lagged Poincaré Plots Indices in Analysis of Heart Rate Variability

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