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 $10^{5}$ 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.


Up: WFDB Applications Guide

Please e-mail your comments and suggestions to, or post them to:

MIT Room E25-505A
77 Massachusetts Avenue
Cambridge, MA 02139 USA

Updated 8 March 2019