  The electrocardiogram (ECG) is one of the most common primary tests to
evaluate the health of the heart. Reliable automatic interpretation of ECG
records is crucial to the goal of improving public health. It can enable a safe
inexpensive monitoring. This work presents a new methodology for morphological
identification of heartbeats, which is placed outside the usual machine
learning framework. The proposal considers the sparsity of the representation
of a heartbeat as a parameter for morphological identification. The approach
involves greedy algorithms for selecting elements from redundant dictionaries,
which should be previously learnt from examples of the classes to be
identified. Using different metrics of sparsity, the dictionary rendering the
smallest sparsity value, for the equivalent approximation quality of a new
heartbeat, classifies the morphology of that beat. This study focuses on a
procedure of learning the dictionaries for representing heartbeats and compares
several metrics of sparsity for morphological identification on the basis of
those metrics. The suitability of the method is illustrated by binary
differentiation of Normal and Ventricular heartbeats in the MIT-BIH Arrhythmia
data set. In general classification 99.7% of the Normal beats and 97.6% of the
Ventricular beats in the testing sets are correctly identified. In interpatient
assessment 91.8% of the Normal beats and 91.0% of Ventricular beats are
correctly identified. Even more important than these scores is the fact that
they are produced on the bases of a single parameter. The numerical tests,
designed to emphasise the interpretability and reliability of the approach,
demonstrate the potential of the method to contribute towards the development
of a well grounded expert system for classification of heartbeats in ECG
records.
