A New Approach to Detect Congestive Heart Failure Using Short-Term Heart Rate Variability Measures

• Published in: PloS one Vol. 9; no. 4; p. e93399
• Main Authors: Liu, Guanzheng, Wang, Lei, Wang, Qian, Zhou, GuangMin, Wang, Ying, Jiang, Qing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2014; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Algorithms; Analysis; Biology and Life Sciences; Biomedical engineering; Cardiac patients; Care and treatment; Case-Control Studies; Classification; Computer and Information Sciences; Congestive heart failure; Diagnosis; Engineering and Technology; Engineering schools; Entropy; Health aspects; Heart failure; Heart Failure - diagnosis; Heart Failure - physiopathology; Heart Rate; Humans; Image retrieval; Laws, regulations and rules; Medical Informatics - methods; Medicine and Health Sciences; Middle Aged; Neural networks; Physical Sciences; Respiration; Risk factors; Studies; Support Vector Machine; Support vector machines; Telemedicine; Time Factors; Time series; Variability
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0093399

Heart rate variability (HRV) analysis has quantified the functioning of the autonomic regulation of the heart and heart's ability to respond. However, majority of studies on HRV report several differences between patients with congestive heart failure (CHF) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measures. In the paper, we mainly presented a new approach to detect congestive heart failure (CHF) based on combination support vector machine (SVM) and three nonstandard heart rate variability (HRV) measures (e.g. SUM_TD, SUM_FD and SUM_IE). The CHF classification model was presented by using SVM classifier with the combination SUM_TD and SUM_FD. In the analysis performed, we found that the CHF classification algorithm could obtain the best performance with the CHF classification accuracy, sensitivity and specificity of 100%, 100%, 100%, respectively.