  The purpose of this study is to optimize the selection of prophylactic
cardioverter defibrillator implantation candidates. Currently, the main
criterion for implantation is a low Left Ventricular Ejection Fraction (LVEF)
whose specificity is relatively poor. We designed two classifiers aimed to
predict, from long term ECG recordings (Holter), whether a low-LVEF patient is
likely or not to undergo ventricular arrhythmia in the next six months. One
classifier is a single hidden layer neural network whose variables are the most
relevant features extracted from Holter recordings, and the other classifier
has a structure that capitalizes on the physiological decomposition of the
arrhythmogenic factors into three disjoint groups: the myocardial substrate,
the triggers and the autonomic nervous system (ANS). In this ad hoc network,
the features were assigned to each group; one neural network classifier per
group was designed and its complexity was optimized. The outputs of the
classifiers were fed to a single neuron that provided the required probability
estimate. The latter was thresholded for final discrimination A dataset
composed of 186 pre-implantation 30-mn Holter recordings of patients equipped
with an implantable cardioverter defibrillator (ICD) in primary prevention was
used in order to design and test this classifier. 44 out of 186 patients
underwent at least one treated ventricular arrhythmia during the six-month
follow-up period. Performances of the designed classifier were evaluated using
a cross-test strategy that consists in splitting the database into several
combinations of a training set and a test set. The average arrhythmia
prediction performances of the ad-hoc classifier are NPV = 77% $\pm$ 13% and
PPV = 31% $\pm$ 19% (Negative Predictive Value $\pm$ std, Positive Predictive
Value $\pm$ std). According to our study, improving prophylactic
ICD-implantation candidate selection by automatic classification from ECG
features may be possible, but the availability of a sizable dataset appears to
be essential to decrease the number of False Negatives.
