This paper addresses the problem of automatic discrimination of
rhythms in ECG signals. In particular we attempt at discriminating
normal sinus rhythm (NSR), ventricular tachycardia (VT) and
ventricular fibrillation (VF). The solution to this problem plays
an important role in determining whether or not a subject should be
given an electroshock. In performing the discrimination we
extract features, computed from segments of the ECG,
that are used as inputs to the learning vector quantization
(LVQ) classification algorithm, possibly with a modified distance measure.
In this paper we show the performance
of three different features for this purpose: the pitch period,
a pulse train extracted from a regular-pulse excitation (RPE)
model of the ECG signal, and, finally, autoregressive (AR) parameters extracted
from the signal.
After presenting classification results, we also comment
on some fundamental problems in testing such classification algorithms.