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RBF Network Classification of ECGs as a Potential Marker for Sudden Cardiac Death

  • H. A. Kestler
  • F. Schwenker
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 67)

Abstract

Non-invasive risk assessment after myocardial infarction is a major but still unresolved goal in clinical cardiology. Various parameters such as ventricular late potentials, T-wave alternans, and repetitive ventricular extrasystoles have been shown to indicate an increased risk of sudden cardiac death. However, the practical use of these arrhythmic markers into clinical decision making remains difficult. In this chapter we will describe two approaches of risk stratification with RBF networks using high-fidelity ECG recordings. Based on these high-fidelity recordings different aspects of conduction defects are exemplarily investigated. The first utilizes established features derived from signal averaged QRS complexes (heartbeats) and the second investigation centers on capturing morphology changes within the QRS complex.

Keywords

Heart Rate Variability Positive Predictive Value Ventricular Tachycardia Negative Predictive Value Sudden Cardiac Death 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • H. A. Kestler
  • F. Schwenker

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