title: A new approach to detect congestive heart failure using short-term heart rate variability measures.
abstract: 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.
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangzhou, China.
authors.forename: Guanzheng
authors.initials: G
authors.lastname: Liu
authors.affiliation: Shenzhen Institutes of Advanced Technology, the Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangzhou, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangzhou, China.
authors.forename: Guangmin
authors.initials: G
authors.lastname: Zhou
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangzhou, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangzhou, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Jiang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016022:Case-Control Studies; D006333:Heart Failure / Q000175:diagnosis* / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D060388:Support Vector Machine; D013997:Time Factors
references.citation: IEEE Trans Biomed Eng. 2007 Oct;54(10):1900-4
references.pmid: 17926691
references.citation: Circulation. 1998 Oct 13;98(15):1510-6
references.pmid: 9769304
references.citation: IEEE Trans Biomed Eng. 2009 Feb;56(2):345-51
references.pmid: 19272924
references.citation: J Am Coll Cardiol. 1992 Jul;20(1):248-54
references.pmid: 1351488
references.citation: Eur Heart J. 1995 Aug;16(8):1100-7
references.pmid: 8665972
references.citation: IEEE Trans Inf Technol Biomed. 2011 Jan;15(1):40-6
references.pmid: 21075731
references.citation: Auton Neurosci. 2000 Dec 28;86(1-2):114-9
references.pmid: 11269916
references.citation: Ann Biomed Eng. 2010 Mar;38(3):854-64
references.pmid: 20012693
references.citation: Auton Neurosci. 2001 Jul 20;90(1-2):102-5
references.pmid: 11485275
references.citation: J Cardiovasc Electrophysiol. 2006 Jun;17(6):691-4
references.pmid: 16836727
references.citation: IEEE Trans Neural Netw. 2011 Apr;22(4):573-87
references.pmid: 21385668
references.citation: Comput Biol Med. 2007 Oct;37(10):1502-10
references.pmid: 17359959
references.citation: Circulation. 1996 Mar 1;93(5):1043-65
references.pmid: 8598068
references.citation: Circulation. 2009 Apr 14;119(14):1977-2016
references.pmid: 19324967
references.citation: Telemed J E Health. 2011 Jun;17(5):348-57
references.pmid: 21545293
references.citation: Telemed J E Health. 2011 Nov;17(9):705-11
references.pmid: 22035321