Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers.
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.
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.
Julia
J
Ramírez
School of Engineering, San Jorge University, Villanueva de Gállego, Spain.
Violeta
V
Monasterio
Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Ana
A
Mincholé
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.
Mariano
M
Llamedo
Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Gustavo
G
Lenis
Department of Electrocardiology, Medical University of Lodz, Sterling Regional Center for Heart Diseases, Lodz, Poland.
Iwona
I
Cygankiewicz
Institut Català de Ciències Cardiovasculars, Santa Creu i Sant Pau Hospital, Barcelona, Spain.
Antonio
A
Bayés de Luna
St. Paul's Cardiac Electrophysiology, University of London, and Imperial College, London, UK.
Marek
M
Malik
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.
Juan Pablo
JP
Martínez
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.
Pablo
P
Laguna
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.
Esther
E
Pueyo
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D016757:Death, Sudden, Cardiac / Q000453:epidemiology*; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D004562:Electrocardiography / Q000379:methods* / Q000706:statistics & numerical data*; D005260:Female; D006333:Heart Failure / Q000175:diagnosis* / Q000401:mortality*; D006801:Humans; D015994:Incidence; D008297:Male; D019300:Medical Errors; D008875:Middle Aged; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013030:Spain / Q000453:epidemiology; D060388:Support Vector Machine; D015996:Survival Rate; D055815:Young Adult
CHF; ECG; Heart rate turbulence; Pump failure death; SCD; Support vector machine; T-wave alternans; Tpe/RR dynamicity