The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.

The impact of the MIT-BIH Arrhythmia Database

The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

ECG Biometric Recognition: A Comparative Analysis

The long-term ST database is the result of a multinational research effort. The goal was to develop a challenging and realistic research resource for development and evaluation of automated systems to detect transient ST segment changes in electrocardiograms and for supporting basic research into the mechanisms and dynamics of transient myocardial ischaemia. Twenty-four hour ambulatory ECG records were selected from routine clinical practice settings in the USA and Europe, between 1994 and 2000, on the basis of occurrence of ischaemic and non-ischaemic ST segment changes. Human expert annotators used newly developed annotation protocols and a specially developed interactive graphic editor tool (SEMIA) that supported paperless editing of annotations and facilitated international co-operation via the Internet. The database contains 86 two- and three-channel 24 h annotated ambulatory records from 80 patients and is stored on DVD-ROMs. The database annotation files contain ST segment annotations of transient ischaemic (1155) and heart-rate related ST episodes and annotations of non-ischaemic ST segment events related to postural changes and conduction abnormalities. The database is intended to complement the European Society of Cardiology ST-T database and the MIT-BIH and AHA arrhythmia databases. It provides a comprehensive representation of 'real-world' data, with numerous examples of transient ischaemic and non-ischaemic ST segment changes, arrhythmias, conduction abnormalities, axis shifts, noise and artifacts.

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Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia.

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The Impact of the MIT-BIH Arrhythmia Database History, Lessons Learned, and Its Influence on Current and Future Databases

A new detector is presented which finds changes in the repolarization phase (ST-T complex) of the cardiac cycle. It operates by applying a detection algorithm to the filtered root mean square (rms) series of differences between the beat segment (ST segment or ST-T complex) and an average pattern segment. The detector has been validated using the European ST-T database, which contains ST-T complex episodes manually annotated by cardiologists, resulting in sensitivity/positive predictivity of 85/86%, and 85/76%, for ST segment deviations and ST-T complex changes, respectively. The proposed detector has a performance similar to those which have a more complicated structure. The detector has the advantage of finding both ST segment deviations and entire ST-T complex changes thereby providing a wider characterization of the potential ischemic events. A post-processing stage, based on a cross-correlation analysis for the episodes in the rms series, is presented. With this stage subclinical events with repetitive pattern were found in around 20% of the recordings and improved the performance to 90/85%, and 89/76%, for ST segment and ST-T complex changes, respectively.

Automatic detection of ST-T complex changes on the ECG using filtered RMS difference series: application to ambulatory ischemia monitoring

Presents the Long Term ST Database, a collection of eighty 24-hour two and three lead ECG records from ambulatory subjects with transient ST segment abnormalities. The database provides a comprehensive standard research resource for quantitatively assessing the performance of automated detectors of transient ischemia, and for supporting basic research into the mechanisms and dynamics of transient ischemia. Records of the database contain annotated significant transient ischemic ST episodes, non-ischemic ST episodes caused by heart rate related changes, non-ischemic ST events due to axis shifts or QRS conduction changes, and individual QRS and rhythm annotations, all made by human experts.

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The Long-Term ST database: a research resource for algorithm development and physiologic studies of transient myocardial ischemia

Reports the status of an ongoing international collaborative research effort to produce a new long term ST database (LTST DB), a collection of seventy annotated ambulatory records containing transient ischemic and non-ischemic ST changes. The authors present the selection criteria for records, an annotation protocol with definitions of transient ST events, interactive graphic tools for manual and automatic annotating, and the annotation procedure.

B. Glavic

Papers

The Long-Term ST database: a research resource for algorithm development and physiologic studies of transient myocardial ischemia

A long-term ST database for development and evaluation of ischemia detectors