Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction

BMC Cardiovascular Disorders

Research Article

Yang Li¹  Lei Lei Yin¹  Tao Song¹  Xiu Fen Qu¹  Bai He Han¹  Wei Cao¹  Jing Yan Piao¹  Heng Da Cheng²  Ying Tao Zhang³ 

[1] Department of Cardiology, the First Affiliated Hospital of Harbin Medical University, No.23 Youzheng Street, 150001, Nangang District, Harbin City, Heilongjiang Province, China;Department of Computer Science, Utah State University, Salt Lake City, UT, USA;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;

关键词: Acute myocardial infarction;    Cardiac death;    Support vector machine;    Heart-rate variability;    Machine learning;   

DOI  :  10.1186/1471-2261-14-59

 received in 2014-02-08, accepted in 2014-04-28,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundPrevious studies indicate that decreased heart-rate variability (HRV) is related to the risk of death in patients after acute myocardial infarction (AMI). However, the conventional indices of HRV have poor predictive value for mortality. Our aim was to develop novel predictive models based on support vector machine (SVM) to study the integrated features of HRV for improving risk stratification after AMI.MethodsA series of heart-rate dynamic parameters from 208 patients were analyzed after a mean follow-up time of 28 months. Patient electrocardiographic data were classified as either survivals or cardiac deaths. SVM models were established based on different combinations of heart-rate dynamic variables and compared to left ventricular ejection fraction (LVEF), standard deviation of normal-to-normal intervals (SDNN) and deceleration capacity (DC) of heart rate. We tested the accuracy of predictors by assessing the area under the receiver-operator characteristics curve (AUC).ResultsWe evaluated a SVM algorithm that integrated various electrocardiographic features based on three models: (A) HRV complex; (B) 6 dimension vector; and (C) 8 dimension vector. Mean AUC of HRV complex was 0.8902, 0.8880 for 6 dimension vector and 0.8579 for 8 dimension vector, compared with 0.7424 for LVEF, 0.7932 for SDNN and 0.7399 for DC.ConclusionsHRV complex yielded the largest AUC and is the best classifier for predicting cardiac death after AMI.

【 授权许可】

Unknown   
© Song et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.