Continuous monitoring of the electrocardiogram in both inpatients and
ambulatory subjects has become a very common procedure during the past
thirty years, with diverse applications ranging from screening for cardiac
arrhythmias or transient ischemia, to evaluation of the efficacy of
antiarrhythmic drug therapy, to surgical and critical care monitoring.
Since the first intensive care units were established in the 1960s,
the need for automated data reduction and analysis of the ECG has been
apparent, motivated by the very large amount of data that must be
analyzed (on the order of 
cardiac cycles per patient per
day). As clinical experience has led to the identification of more
and more prognostic indicators in the ECG, clinicians have demanded
and received increasingly sophisticated automated ECG analyzers. The
early heart rate monitors rapidly evolved into devices that were
designed first to detect ventricular fibrillation, then other
``premonitory'' ventricular arrhythmias. Many newer devices attempt
to detect supraventricular arrhythmias and transient ischemic ST
changes.
Visual analysis of the ECG is far from simple. Accurate diagnosis of ECG abnormalities requires attention to subtle features of the signals, features that may appear only rarely, and which are often obscured by or mimicked by noise. Diagnostic criteria are complicated by inter- and intra-patient variability of both normal and abnormal ECG features. Given these considerations, it is not surprising that developers are faced with a difficult task in the design of algorithms for automated ECG analysis, and that the results of their efforts are imperfect. Certain parts of the problem -- QRS detection in the absence of noise, for example -- are well-solved by most current algorithms; others -- detection of supraventricular arrhythmias, for example -- remain exceedingly difficult. Just as we may find it easiest to analyze ``textbook'' examples, automated ECG analyzers may perform better while analyzing the recordings used during their development than when applied to ``real-world'' signals.
Since automated ECG analyzers vary in performance, and since their performance is dependent on the characteristics of their input, quantitative evaluations of these devices are essential in order to assess the usefulness of their outputs. At one extreme, a device's outputs in the context of a particular type of signal may be so unreliable as to be worthless; unfortunately, the other extreme -- an output so reliable it can be accepted uncritically -- is not a characteristic of any existing monitor, nor can it be expected in the future.