Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

BioMed Research International
Lal HussainKyung Sup Kwak

Abstract

The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07,...Continue Reading

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Citations

Sep 22, 2020·Computer Methods in Biomechanics and Biomedical Engineering·Ahmet Çınar, Seda Arslan Tuncer
Jan 20, 2021·Journal of Sleep Research·Seren UcakPeter A Cistulli
Feb 3, 2021·Mathematical Biosciences and Engineering : MBE·Lal HussainJalal S Alowibdi
Apr 25, 2021·Mathematical Biosciences and Engineering : MBE·Sadia AnjumTim Q. Duong

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