  In this work, we propose an abductive framework for biosignal interpretation,
based on the concept of Temporal Abstraction Patterns. A temporal abstraction
pattern defines an abstraction relation between an observation hypothesis and a
set of observations constituting its evidence support. New observations are
generated abductively from any subset of the evidence of a pattern, building an
abstraction hierarchy of observations in which higher levels contain those
observations with greater interpretative value of the physiological processes
underlying a given signal. Non-monotonic reasoning techniques have been applied
to this model in order to find the best interpretation of a set of initial
observations, permitting even to correct these observations by removing, adding
or modifying them in order to make them consistent with the available domain
knowledge. Some preliminary experiments have been conducted to apply this
framework to a well known and bounded problem: the QRS detection on ECG
signals. The objective is not to provide a new better QRS detector, but to test
the validity of an abductive paradigm. These experiments show that a knowledge
base comprising just a few very simple rhythm abstraction patterns can enhance
the results of a state of the art algorithm by significantly improving its
detection F1-score, besides proving the ability of the abductive framework to
correct both sensitivity and specificity failures.
