title: Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction.
abstract: 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.
authors.forename: Tao
authors.initials: T
authors.lastname: Song
authors.affiliation: Department of Cardiology, the First Affiliated Hospital of Harbin Medical University, No,23 Youzheng Street, Nangang District, Harbin City 150001, Heilongjiang Province, China. xiufenq@sina.cn.
authors.forename: Xiu Fen
authors.initials: XF
authors.lastname: Qu
authors.forename: Ying Tao
authors.initials: YT
authors.lastname: Zhang
authors.forename: Wei
authors.initials: W
authors.lastname: Cao
authors.forename: Bai He
authors.initials: BH
authors.lastname: Han
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.forename: Jing Yan
authors.initials: JY
authors.lastname: Piao
authors.forename: Lei Lei
authors.initials: LL
authors.lastname: Yin
authors.forename: Heng
authors.initials: H
authors.lastname: Da Cheng
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D003661:Decision Support Techniques; D015716:Electrocardiography, Ambulatory; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000401:mortality / Q000503:physiopathology / Q000628:therapy; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012307:Risk Factors; D013318:Stroke Volume; D060388:Support Vector Machine; D013997:Time Factors; D016277:Ventricular Function, Left