Mining Biosignal Data: Coronary Artery Disease Diagnosis Using Linear and Nonlinear Features of HRV

Lee, Heon Gyu; Noh, Ki Yong; Ryu, Keun Ho

doi:10.1007/978-3-540-77018-3_23

Cited by 67 publications

(37 citation statements)

References 11 publications

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“…Patidar et al presented a new method for diagnosis of CAD using tunable-Q wavelet transform based features extracted from heart rate signals [49]. Many linear and nonlinear parameters are extracted from heart rate signals and used as diagnostic features to predict the subjects with CAD [3][4][5][6], the features were then fed into classifiers for automated diagnosis of CAD subjects [4][5][6]. In our previous publication, we have reported the univariate analysis results for RRI and DTI (analysis of RRI is also performed in this study, the results are shown in Table S1 in supplementary materials), using sample entropy, fuzzy entropy, and refined fuzzy entropy.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have studied the linear and nonlinear features of HRV for different positions (e.g., the supine, left lateral and right lateral positions), and the features were fed into classifiers for the purpose of classifying normal and coronary artery disease (CAD) states in previous studies [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…purpose of classifying normal and coronary artery disease (CAD) states in previous studies [3][4][5][6]. Systole (heart contraction) and diastole (heart relaxation), apart from heartbeat which primarily reflects the electrical activity of the heart, are fundamental mechanical cardiac activities.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

Ji¹,

Liu

et al. 2016

Entropy

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Coronary artery disease (CAD) is the most common cause of death globally. To detect CAD noninvasively at an early stage before clinical symptoms occur is still nowadays challenging. Analysis of the variation of heartbeat interval (RRI) opens a new avenue for evaluating the functional change of cardiovascular system which is accepted to occur at the subclinical stage of CAD. In addition, systolic time interval (STI) and diastolic time interval (DTI) also show potential. There may be coupling in these electromechanical time series due to their physiological connection. However, to the best of our knowledge no publication has systematically investigated how can the coupling be measured and how it changes in CAD patients. In this study, we enrolled 39 CAD patients and 36 healthy subjects and for each subject the electrocardiogram (ECG) and photoplethysmography (PPG) signals were recorded simultaneously for 5 min. The RRI series, STI series, and DTI series were constructed, respectively. We used linear cross correlation (CC), coherence function (CF), as well as nonlinear mutual information (MI), cross conditional entropy (XCE), cross sample entropy (XSampEn), and cross fuzzy entropy (XFuzzyEn) to analyse the bivariate RRI-DTI coupling, RRI-STI coupling, and STI-DTI coupling, respectively. Our results suggest that the linear CC and CF generally have no significant difference between the two groups for all three types of bivariate coupling. The MI only shows weak change in RRI-DTI coupling. By comparison, the three entropy-based coupling measurements show significantly decreased coupling in CAD patients except XSampEn for RRI-DTI coupling (less significant) and XCE for STI-DTI and RRI-STI coupling (not significant). Additionally, the XFuzzyEn performs best as it was still significant if we further applied the Bonferroni correction in our statistical analysis. Our study indicates that the intrinsic electromechanical coupling is most probably nonlinear and can better be measured by nonlinear entropy-based measurements especially the XFuzzyEn. Besides, CAD patients are accompanied by a loss of electromechanical coupling. Our results suggest that cardiac electromechanical coupling may potentially serve as a noninvasive diagnostic tool for CAD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

Ji¹,

Liu

et al. 2016

Entropy

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“…Only explainable results (e.g. rule based results) are easy to understand and applicable to the bio-medical and diagnosis of a disease [23], [24]. A rule is a set of conjunctive conditions with a predictive term.…”

Section: Evolutional Diagnostic Rules Miningmentioning

confidence: 99%

Evolutional Diagnostic Rules Mining for Heart Disease Classification Using ECG Signal Data

Piao

Shon

et al. 2012

Lecture Notes in Electrical Engineering

Self Cite

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Abstract. Medical information related data sets are useful for the diagnosis and treatment of disease. With the development of technology and devices in biomedical engineering, it leads data overflow nowadays. Traditional data mining methods like SVM, ANN and decision tree are applied to perform the classification of arrhythmia disease. However, traditional analysis methods are far beyond the capacity and speed to deal with large scale of information. Techniques that have capability to handle the coming data sets in incremental learning phase can solve those problems. Therefore, in this paper, we proposed an incremental decision trees induction method which uses ensemble method for mining evolutional diagnostic rules for cardiac arrhythmia classification. Experimental results show that our proposed method performs better than other algorithms in our study.

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“…Linear and non linear parameters were extracted and classified using SVM classifier [33]. Using empirical mode decomposition and Teager energy operator, on normal and CAD heart rate variability signals were classified using back propagation neural network [46].…”

Section: Introductionmentioning

confidence: 99%

Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

Giri

Acharya

Martis

et al. 2013

Knowledge-Based Systems

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107

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Mining Biosignal Data: Coronary Artery Disease Diagnosis Using Linear and Nonlinear Features of HRV

Cited by 67 publications

References 11 publications

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

Evolutional Diagnostic Rules Mining for Heart Disease Classification Using ECG Signal Data

Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

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