Ambulatory electrocardiographic (AECG) monitoring is widely used for analysis of transient ST-segment and T-wave changes compatible with ischemia. Most AECG instruments do not attempt to distinguish between ischemic and non-ischemic ST and T changes, however, because of a lack of standard definitions of transient ST-T events and knowledge about their meaning.
In order to study these events, and to evaluate and compare automated methods for their detection and interpretation, the ICP group in Pisa defined diagnostic criteria for transient ST and T changes, and a protocol for annotating them [1]. This group took the leading role in the development of the European Society of Cardiology ST-T Database (ESC DB) [2], which was the first generally available set of well-characterized, representative ECG recordings with documented ischemic and non-ischemic ST and T changes. The ESC DB has proven to be an invaluable tool for designers and evaluators of automated ischemia detectors. Its availability has stimulated extensive research and publication in this field during the past several years, including recognition algorithms based on time-domain analysis, the Karhunen-Loève Transform (KLT), neural networks, and fuzzy logic.
The ESC DB contains 90 two-hour, two-channel ambulatory records with 368 documented transient ischemic ST episodes, but only 11 non-ischemic ST episodes. Non-ischemic ST episodes, which are of no clinical interest per se, account for many of the false positives of automated ischemic ST detectors. Thus it is particularly important to understand these events and to define their distinctive characteristics in order to improve detector performance. The small number of non-ischemic episodes in the ESC DB does not permit exhaustive study of these differences, however.
Furthermore, our previous study on characterization of transient ST
segment changes in the ESC DB[3], revealed two additional
types of important ST events. We found three cases of ``mixed episodes''
(non-ischemic episodes containing ischemic episodes within), and 17
cases of significant (
100
V) slow drift of ST deviation level
(15 of which also contain ischemic episodes). We also
described striking and varied temporal patterns of transient ischemic
ST changes. These observations provoke questions regarding the
relationships between these patterns and the underlying mechanisms
that are responsible for ischemia. We cannot answer these questions
definitively, however, since we are not able to observe more than a
handful of repetitions of each pattern in the two-hour segments of
the ESC DB.
We have therefore begun to prepare a new, long-term, ambulatory ST database, in order to support the development and evaluation of detectors capable of more accurate differentiation of ischemic and non-ischemic ST changes, and to provide more examples of non-ischemic episodes, episodes of slow ST level drift and mixed episodes.