Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis

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

doi:10.1109/embc.2015.7318361

Cited by 5 publications

(6 citation statements)

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“…All patients were studied according to a protocol, previously approved by the Hospital ethics committee. Information about cardiomyopathy patients database is presented in [4], [10]. The control group, 36 healthy subjects, were recorded at the Institute for Bioengineering of Catalonia, Barcelona, Spain (according to the Institutional Review Board), using BIOPAC Systems Inc. MP150 for the ECG and RF signals, and Finometer-Finapres Medical Systems for BP signal, for 15 minutes, at a sampling frequency of 500 Hz, in supine position.…”

Section: Databasementioning

confidence: 99%

“…In our previous works, we have proposed to analysis cardiorespiratory and cardiovascular interactions in patients with cardiomyopathy, through time-and frequency domain parameters, or using joint symbolic dynamics analysis [4], [10]. In this work we propose new parameters to classify patients with ischemic and dilated cardiomyopathies, according to low and high cardiac risk in function of the left ventricular ejection fraction (LVEF).…”

Section: Introductionmentioning

confidence: 99%

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

Rodriguez

Voss

Caminal³

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and T time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.

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Section: Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rodriguez

Voss

Caminal³

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…In JSD, the increments between two successive values of the temporal series are coded as “1” and the decrements and equilibriums are coded as “0,” these increments and decrements are considered in relation to a threshold l . The vector X can be transformed into the symbolic vector S using the rules set out in Equation (2), were the threshold l is set equal to 0 (Baumert et al, 2002; Giraldo et al, 2015).…”

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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“…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%

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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Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis

Cited by 5 publications

References 0 publications

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

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

Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis

Cardiovascular Coupling-Based Classification of Ischemic and Dilated Cardiomyopathy Patients

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