ECG Databases

Several databases of ECG recordings are generally available for evaluating ECG analyzers. They serve several important needs:

• They contain representative signals. Wide variations in ECG characteristics among subjects severely limit the value of synthesized waveforms for testing purposes. Realistic tests of ECG analyzers require large sets of “real-world” signals.

• They contain rarely observed but clinically significant signals. Although it is not particularly difficult to obtain recordings of common ECG abnormalities, often those that are most significant are rarely recorded. Both developers and evaluators of ECG analyzers need examples of such recordings.

• They contain standard signals. System comparisons are meaningless unless performance is measured using the same test data in each case, since performance is so strongly data-dependent.

• They contain annotated signals. Typically, each QRS complex has been manually annotated by two or more cardiologists working independently. The reference annotations produced as a result serve as a “gold standard” against which a device's analysis can be compared quantitatively.

• They contain digitized, computer-readable signals. It is therefore possible to perform a fully automated, strictly reproducible test in the digital domain if desired, allowing one to establish with certainty the effects of algorithm modifications on performance.

Standards EC38 and EC57 require the use of the following ECG databases:¹

• AHA DB: The American Heart Association Database for Evaluation of Ventricular Arrhythmia Detectors (80 records, 35 minutes each)

• MIT DB: The Massachusetts Institute of Technology–Beth Israel Hospital Arrhythmia Database (48 records, 30 minutes each)

• ESC DB: The European Society of Cardiology ST-T Database (90 records, two hours each)

• NST DB: The Noise Stress Test Database (12 records, 30 minutes each)

• CU DB: The Creighton University Sustained Ventricular Arrhythmia Database (35 records, 8 minutes each)

Each of these databases represents a very substantial effort by many workers; in particular, the AHA, MIT, and ESC databases each required more than five years of sustained effort by large teams of researchers and clinicians from many institutions. Nevertheless, it should be recognized that even these databases do not fully represent the variety of “real-world” ECGs observed in clinical practice. Although these databases permit standardized, quantitative, automated, and fully reproducible evaluations of analyzer performance, it is risky to extrapolate from the results of such evaluations to expectations of real-world performance. Such extrapolations can be particularly error-prone if the evaluation data were also used for development of the analysis algorithm, since the algorithm may have been (perhaps unintentionally) “tuned” to its training set. It should also be noted that the first four of the databases listed above were obtained from Holter ECG recordings; although the frequency response of the Holter recording technique is not usually a limiting factor in the performance of an ECG analyzer, it may tend to favor devices that are designed to analyze Holter recordings over devices that have been designed to analyze higher-fidelity input signals.

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Updated 10 June 2022