Abstract
Two methods for diagnostic classification of the electrocardiogram are described:
a heuristic one and a statistical one. In the heuristic approach, the cardiologist
provides the knowledge to construct a classifier, usually a decision tree. In the
statistical approach, probability densities of diagnostic features are estimated from
a learning set of ECGs and multivariate techniques are used to attain diagnostic classification.
The relative merits of both approaches with respect to criteria selection, comprehensibility,
flexibility, combined diseases, and performance are described. Optimization of heuristic
classifiers is discussed. It is concluded that heuristic classifiers are more comprehensible
than statistical ones; encounter less difficulties in dealing with combined categories;
are flexible in the sense that new categories may readily be added or that existing
ones may be refined stepwise. Statistical classifiers, on the other hand, are more
easily adapted to another operating environment and require less involvement of cardiologists.
Further research is needed to establish differences in performance between both methods.
In relation to performance testing the issue is raised whether the ECG should be classified
using as much prior information as possible, or whether it should be classified on
itself, explicitly discarding information other than age and sex, while only afterwards
other information will be used to reach a final diagnosis. Consequences of taking
one of both positions are discussed.
Key-Words
ECG Classification - Optimization - Gold Standard
