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.
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Updated 26 January 2018