Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers

Julia Ramírez; Violeta Monasterio; Ana Mincholé; Mariano Llamedo; Gustavo Lenis; Iwona Cygankiewicz; Antonio Bayés de Luna; Marek Malik; Juan Pablo Martínez; Pablo Laguna; Esther Pueyo

doi:10.1016/j.jelectrocard.2015.04.002

Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers

J Electrocardiol. 2015 Jul-Aug;48(4):551-7. doi: 10.1016/j.jelectrocard.2015.04.002. Epub 2015 Apr 9.

Authors

Affiliations

¹ Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Zaragoza, Spain. Electronic address: Julia.Ramirez@unizar.es.
² School of Engineering, San Jorge University, Villanueva de Gállego, Spain.
³ Department of Computer Science, University of Oxford, Oxford, United Kingdom.
⁴ Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Zaragoza, Spain.
⁵ Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
⁶ Department of Electrocardiology, Medical University of Lodz, Sterling Regional Center for Heart Diseases, Lodz, Poland.
⁷ Institut Català de Ciències Cardiovasculars, Santa Creu i Sant Pau Hospital, Barcelona, Spain.
⁸ St. Paul's Cardiac Electrophysiology, University of London, and Imperial College, London, UK.
⁹ Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Zaragoza, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain.

PMID: 25912974
DOI: 10.1016/j.jelectrocard.2015.04.002

Abstract

Background: Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients 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 (Δα), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD.

Methods: Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Δα, IAA and TS were obtained. A strategy was implemented using support vector machines (SVM) to classify patients in three groups: SCD victims, PFD victims and other patients (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier.

Results: Δα and IAA, dichotomized at 0.035 (dimensionless) and 3.73 μV, respectively, were the ECG markers most strongly associated with SCD, while TS, dichotomized at 2.5 ms/RR, was the index most strongly related to PFD. When separating SCD victims from the rest of patients, the individual marker with best performance was Δα≥0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Δα and IAA increased Se to 18%. For separation of PFD victims from the rest of patients, the best individual marker was TS ≤ 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 Δα and TS. 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 SCD and Sp of 81% (Se=14%) for PFD.

Conclusions: The results shown in this work suggest that it is possible to efficiently discriminate SCD and PFD in a population of CHF patients using ECG-derived risk markers like Δα, TS and IAA.

Keywords: CHF; ECG; Heart rate turbulence; Pump failure death; SCD; Support vector machine; T-wave alternans; Tpe/RR dynamicity.

Publication types

Multicenter Study
Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Aged
Aged, 80 and over
Comorbidity
Death, Sudden, Cardiac / epidemiology*
Diagnosis, Computer-Assisted / statistics & numerical data*
Electrocardiography / methods*
Electrocardiography / statistics & numerical data*
Female
Heart Failure / diagnosis*
Heart Failure / mortality*
Humans
Incidence
Male
Medical Errors
Middle Aged
Reproducibility of Results
Risk Factors
Sensitivity and Specificity
Spain / epidemiology
Support Vector Machine
Survival Rate
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding