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

BMC Cardiovascular Disorders

Heng Da Cheng¹  Lei Lei Yin³  Jing Yan Piao³  Yang Li³  Bai He Han³  Wei Cao³  Ying Tao Zhang²  Xiu Fen Qu³  Tao Song³ 

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

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

Others  :  855093
DOI  :  10.1186/1471-2261-14-59

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

【 摘 要 】

Background

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.

Methods

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).

Results

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.

Conclusions

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

【 授权许可】

   
2014 Song et al.; licensee BioMed Central Ltd.

【 图 表 】

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