Automatic SVM classification of @entity1164 and XXXX from autonomic and repolarization ECG markers.

@entity1164 @entity1 @entity712 @entity1950 @entity204
BACKGROUND: Considering the rates of @entity1164 ( @entity1164 ) and @entity204 ( @entity204 ) in @entity712 ( @entity712 ) @entity1 and the cost-effectiveness of their preventing treatments, identification of @entity712 @entity1 at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (  a), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence ( @entity1950 ) for classification of @entity1164 and @entity204 . METHODS: Holter ECG recordings of 597 @entity712 @entity1 with sinus rhythm enrolled in the MUSIC study were analyzed and   a, IAA and @entity1950 were obtained. A strategy was implemented using support vector machines (SVM) to classify @entity1 in three groups: @entity1164 victims, @entity204 victims and other @entity1 (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier. RESULTS:   a and IAA, dichotomized at 0.035 (dimensionless) and 3.73  V, respectively, were the ECG markers most strongly associated with @entity1164 , while @entity1950 , dichotomized at 2.5 ms/RR, was the index most strongly related to @entity204 . When separating @entity1164 victims from the rest of @entity1 , the individual marker with best performance was   a>= 0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of   a and IAA increased Se to 18%. For separation of @entity204 victims from the rest of @entity1 , the best individual marker was @entity1950  <= 2.5 ms/RR, which, for Sp=90%, showed a Se of 26%, this value being lower than Se=34%, produced by the combination of   a and @entity1950 . Furthermore, when performing SVM classification into the three reported groups, the optimal combination of risk markers led to a maximum Sp of 79% (Se=18%) for @entity1164 and Sp of 81% (Se=14%) for @entity204 . CONCLUSIONS: The results shown in this work suggest that it is possible to efficiently discriminate @entity1164 and @entity204 in a population of @entity712 @entity1 using ECG-derived risk markers like   a, @entity1950 and IAA.
