Characterization and classification of patients with different levels of cardiac death risk by using Poincaré plot analysis

Rodriguez, Javier; Voss, Andreas; Caminal, P.; Bayés‐Genís, Antoni; Giraldo, Beatriz F.

doi:10.1109/embc.2017.8037078

Cited by 9 publications

(13 citation statements)

References 9 publications

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“…The typical representation is an elongated scatter of plots through the line of identity with all the points whose values are near the mean placed toward the center. Long- and short-term variability indices can be by fitting an ellipse to the shape of the plot and measuring the dispersion along the minor (SD1) and major (SD2) axis of the ellipse, corresponding to their standard deviation (Rodriguez et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis

et al. 2019

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Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDC HR ) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDC LR ) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDC HR patients presented lowered HRV and increased BPV compared to both IDC LR patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDC LR vs. IDC HR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.

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Section: Methodsmentioning

confidence: 99%

Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis

et al. 2019

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“…The indices of long-and short-term variability can be evaluated by calculating the standard deviation across the perpendicular axis (SD1) and the longitudinal direction (SD2), respectively [6]. SD1 and SD2 are given by (3)…”

Section: − High Resolution Joint Symbolic Dynamicsmentioning

confidence: 99%

“…If = { 1 , … , }, ∈ ℝ is a given set of data vectors and = { 1 , … , } their corresponding labels. The SVM function, defined as a linear discriminant function, known as hyperplane, is given by (6) ( ) = + = ∑ ( ) + ,…”

Section: Classificationmentioning

confidence: 99%

“…The indices and defines the efficiency of the classifier on optimal values, and σ is a penalization term. The kernels considered were the Gaussian, Laplace and ANOVA [6]. The model was then optimized by maximizing the margin while minimizing the training error.…”

Section: Classificationmentioning

confidence: 99%

“…In our previous work, we analyzed the cardiovascular interactions in cardiomyopathy patients using different univariate linear and non-linear methods [5][6][7]. In this study, we propose this analysis using coupling-based non-linear methods.…”

Section: Introductionmentioning

confidence: 99%

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Cardiovascular Coupling-Based Classification of Ischemic and Dilated Cardiomyopathy Patients

Rodriguez

Schulz

Voss

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Cardiovascular diseases are one of the most common causes of death in elderly patients. The etiology of cardiomyopathies is difficult to discern clinically. The objective of this study was to classify cardiomyopathy patients using coupling analysis, through their cardiovascular behavior and the baroreflex response. A total of thirty-eight cardiomyopathy patients (CMP) classified as ischemic (ICM, 25 patients) and dilated (DCM, 13 patients) were analyzed. Thirty elderly control subjects (CON) were used as reference. Their electrocardiographic (ECG) and blood pressure (BP) signals were studied. To characterize the cardiovascular activity, the following temporal series were extracted: beat-to-beat intervals (from the ECG signal), and end-systolic and diastolic blood pressure amplitudes (from the BP signal). Non-linear characterization techniques like high resolution joint symbolic dynamics, segmented Poincaré plot analysis, normalized shorttime partial directed coherence, and dual sequence method were used to characterize these times series. The best indices were used to build support vector machine models for classification. The optimal model for ICM versus DCM patients achieved 84.2% accuracy, 76.9% sensitivity, and 88% specificity. When CMP patients and CON subjects were compared, the best model achieved 95.5% accuracy, 97.3% sensitivity, and 93.3% specificity. These results suggest a disfunction in the baroreflex mechanism in cardiomyopathies patients.

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Revisiting Left Ventricular Ejection Fraction Levels: A Circadian Heart Rate Variability-Based Approach

Alkhodari¹,

Jelinek

Saleem

et al. 2021

IEEE Access

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Analysis of heart failure is important in clinical practice to ensure coronary artery disease (CAD) patients will be provided with appropriate timely treatment. The current gold-standard, echocardiography, although reliable, provides a once-off left ventricular ejection fraction (LVEF) measurement and does not provide information about heart function disturbances during day / night cardiac cycles. Additionally, the discrimination between heart failure with preserved and mid-range ejection fraction remains challenging in echocardiography tests. In this vein, this study was sought to investigate the ability of heart rate variability (HRV) in categorizing CAD patients into multiple LVEF groups throughout the 24-hour circadian cycle and checking its agreement with established gold-standard echocardiography-based guidelines. A total of 92 CAD patients who have suffered from heart failure were included in this study. The newly introduced index, HRV ejection fraction (HRVEF), was based on optimizing indices extracted from HRV data, which are correlated with the sympathetic and parasympathetic nervous systems, to form group membership of the preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) LVEF categories. HRVEF groups optimized on hourly basis through Jenks natural breaks algorithm exhibited a consistent pattern with a goodness of variance fit (GVF) of more than 70% accuracy during the late-night to early-morning (01:00-08:00) and evening (17:00-23:00) time periods. At these hours, several HRV indices were found significant (p-value ≤ 0.05) in differentiating between HRVEF groups using statistical analysis of variance (ANOVA) test. These features include the successive differences between normal heartbeats (RMSSD), low and high frequency (LF, HF) power, standard deviation of normal heartbeats (SD2), short-term scaling exponent (alpha1), and percentage of normal heartbeats in alternation segments (PAS). The findings of this study suggest HRV as a promising supplementary tool to the once-off echocardiography for timely LVEF measurements and heart failure prognosis. It paves the way towards multi-time HRV-based estimations for LVEF according to the association between LVEF and HRV indices to better demonstrate the circadian cardiac function at different LVEF levels in CAD patients.

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Characterization and classification of patients with different levels of cardiac death risk by using Poincaré plot analysis

Cited by 9 publications

References 9 publications

Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis

Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis

Cardiovascular Coupling-Based Classification of Ischemic and Dilated Cardiomyopathy Patients

Revisiting Left Ventricular Ejection Fraction Levels: A Circadian Heart Rate Variability-Based Approach

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