TY - JOUR A2 - Noman, Nasimul AU - Hussain, Lal AU - Awan, Imtiaz Ahmed AU - Aziz, Wajid AU - Saeed, Sharjil AU - Ali, Amjad AU - Zeeshan, Farukh AU - Kwak, Kyung Sup PY - 2020 DA - 2020/02/18 TI - Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques SP - 4281243 VL - 2020 AB - 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, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients. SN - 2314-6133 UR - https://doi.org/10.1155/2020/4281243 DO - 10.1155/2020/4281243 JF - BioMed Research International PB - Hindawi KW - ER -