Open Access Open Badges Research article

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

Tao Song1, Xiu Fen Qu1*, Ying Tao Zhang2, Wei Cao1, Bai He Han1, Yang Li1, Jing Yan Piao1, Lei Lei Yin1 and Heng Da Cheng3

Author Affiliations

1 Department of Cardiology, the First Affiliated Hospital of Harbin Medical University, No.23 Youzheng Street, Nangang District, Harbin City 150001, Heilongjiang Province, China

2 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

3 Department of Computer Science, Utah State University, Salt Lake City, UT, USA

For all author emails, please log on.

BMC Cardiovascular Disorders 2014, 14:59  doi:10.1186/1471-2261-14-59

Published: 1 May 2014



Previous 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.


A 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).


We 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.


HRV complex yielded the largest AUC and is the best classifier for predicting cardiac death after AMI.

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