Cardiorespiratory and Vascular Variability Analysis to Classify Patients with Ischemic and Dilated Cardiomyopathy.
Insights
Cardiomyopathy patients were classified using cardio, respiratory, and vascular variability. Support vector machine models accurately distinguished between ischemic and dilated cardiomyopathy, and between patients and controls.
Area of Science:
- Cardiology
- Biomedical Engineering
- Physiology
Background:
- Heart disease is a leading cause of death globally.
- Diagnosing the specific cause of cardiomyopathies remains difficult.
- Cardiomyopathy patients exhibit distinct physiological variability patterns.
Purpose of the Study:
- To classify cardiomyopathy patients using variability analysis.
- To differentiate between ischemic (ICM) and dilated (DCM) cardiomyopathy.
- To assess baroreflex response through vascular activity.
Main Methods:
- Analysis of electrocardiographic, respiratory, and blood pressure signals.
- Extraction of beat-to-beat intervals (BBI), respiratory cycle time (TT), systolic (SBP), and diastolic (DBP) blood pressure.
- Geometric and statistical characterization of cardiorespiratory and vascular activity.
- Support vector machine (SVM) model development for classification.
Main Results:
- Optimal SVM model achieved 92.7% accuracy for ICM vs. DCM classification.
- SVM model achieved 86.2% accuracy when comparing cardiomyopathy patients (CMP) to controls (CON).
- Classification accuracy, sensitivity, and specificity were high for both comparisons.
Conclusions:
- Cardio, respiratory, and vascular variability analysis effectively classifies cardiomyopathy subtypes.
- Physiological variability patterns differ significantly between ICM and DCM patients.
- Patients show impaired regulation of vascular variability, indicating limited baroreflex response.
Abstract:
Heart diseases are the leading cause of death in developed countries. Ascertaining the etiology of cardiomyopathies is still a challenge. The objective of this study was to classify cardiomyopathy patients through cardio, respiratory and vascular variability analysis, considering the vascular activity as the input and output of the baroreflex response. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM, 24 patients) and dilated (DCM, 17 patients) were analyzed. Thirty-nine elderly control subjects (CON) were used as reference. From the electrocardiographic, respiratory flow, and blood pressure signals, following temporal series were extracted: beat-to-beat intervals (BBI), total respiratory cycle time series (TT), and end- systolic (SBP) and diastolic (DBP) blood pressure amplitudes, respectively. Three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. These results suggest a limited ability of cardiac and respiratory systems response to regulate the vascular variability in these patients.
Related Concept Videos
Cardiomyopathy I: Introduction and Classification
Cardiomyopathy II: Dilated Cardiomyopathy
Cardiomyopathy IV: Restrictive Cardiomyopathy
Cardiomyopathy III: Hypertrophic Cardiomyopathy
Cardiomyopathy V: Interprofessional Care
Heart Failure IV: Classification and Diagnostic Evaluation


