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Journal ArticleDOI

The impact of the MIT-BIH Arrhythmia Database

01 May 2001-IEEE Engineering in Medicine and Biology Magazine (IEEE Eng Med Biol Mag)-Vol. 20, Iss: 3, pp 45-50
TL;DR: The history of the database, its contents, what is learned about database design and construction, and some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database are reviewed.
Abstract: 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.
Citations
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Journal ArticleDOI
TL;DR: A fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system that achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopy beats.
Abstract: Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.

1,300 citations

Journal ArticleDOI
TL;DR: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records.
Abstract: Objective: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Design: Data collection and retrospective analysis. Setting: All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. Patients: Adult patients admitted to intensive care units between 2001 and 2007. Interventions: None. Measurements and Main Results: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user’s guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1‐4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). Conclusions: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. (Crit Care Med 2011; 39:952‐960)

960 citations


Cites methods from "The impact of the MIT-BIH Arrhythmi..."

  • ...MIMIC-II may serve a role analogous to the public access arrhythmia databases that played an indispensable role in the development, refinement, and—ultimately—widespread acceptance of automated algorithms for electrocardiogram analysis [21]....

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  • ...For example, the ECG database that supported development and evaluation of automated arrhythmia algorithms included much smaller data collections (48 half-hour samples in the MIT-BIH Arrhythmia Database [21], and 80 3-hour records in the AHA ECG Database) [23]....

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Journal ArticleDOI
TL;DR: This paper proposes to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud and presents a prototype of a Smart e-Health Gateway called UT-GATE.

867 citations


Cites methods from "The impact of the MIT-BIH Arrhythmi..."

  • ...We use MIT-BIT Arrhythmia database [65] which includes abundant ECG data sources sampled at 360 samples per second in 11-bit over 10 mV....

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Journal ArticleDOI
TL;DR: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

703 citations


Cites background or methods from "The impact of the MIT-BIH Arrhythmi..."

  • ..., 2017 [134] ECG identification DNN MIT arrhythmia database [135] and self-collected data 94....

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  • ..., 2017 [136] Arrhythmia classification Robust deep dictionary learning MIT arrhythmia database [135] 97....

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  • ..., 2017 [139] Identification of ventricular arrhythmias CNN MIT-BIH arrhythmia DB (MITDB) [129, 135], MITBIH malignant ventricular arrhythmia DB (VFDB) [140], Creighton University ventricular tachyarrhythmia DB (CUDB) [141] accuracy: 92....

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  • ..., 2017 [138] Heartbeat classification RBM MIT-BIH arrhythmia DB [135] RBM-based model to extract representative features and to reduce the data dimensionality Muduli et al....

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  • ..., 2017 [138] Heartbeat classification DNN Lead II from the MIT-BIH arrhythmia DB [135] Accuracy: 97....

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Journal ArticleDOI
TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.

635 citations


Cites background from "The impact of the MIT-BIH Arrhythmi..."

  • ...It was also the first database available for this goal and has been constantly refined along the years [148]....

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  • ...More information regarding this database can be found in [148]....

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References
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Journal ArticleDOI
20 Oct 1961-Science
TL;DR: Electrocardiography is proposed to be implemented by the use of long-period, continuous recording of heart potentials with a portable, self-contained instrument together with semiautomatic methods for the rapid analysis of the resulting voluminous data.
Abstract: I have proposed that orthodox electrocardiography be implemented, both for research and medical purposes, by the use of long-period, continuous recording of heart potentials with a portable, self-contained instrument-the electrocardiocorder together with semiautomatic methods for the rapid analysis of the resulting voluminous data. An electronic system to make this concept practical has been developed in our laboratory and typical results are described in this article.

464 citations

Proceedings ArticleDOI
23 Sep 1990
TL;DR: A compact-disk ROM containing the Massachusetts Institute of Technology (MIT)-Boston's Beth Israel Hospital (BIH) Arrhythmia Database as well as a large number of supplementary recordings assembled for various research projects was produced.
Abstract: A compact-disk ROM containing the Massachusetts Institute of Technology (MIT)-Boston's Beth Israel Hospital (BIH) Arrhythmia Database as well as a large number of supplementary recordings assembled for various research projects was produced. In all, the CD-ROM contains approximately 600 megabytes of digitized electrocardiograms (ECG) recordings, most with beat-by-beat annotations, having a total duration in excess of 200 hours. The CD-ROM format makes this substantial collection of ECGs accessible to researchers with PCs as well as those with larger computer systems. The contents of the CD-ROM and the issues involved in its production are described. Software for use with the CD-ROM as well as for development of similar databases is also described. >

367 citations


"The impact of the MIT-BIH Arrhythmi..." refers background in this paper

  • ...Approximately 400 copies of these CD-ROMs have been distributed to date [4]....

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Journal ArticleDOI
TL;DR: What PhysioNet offers to researchers is discussed, some of the technology needed to support these functions are described, and observations gleaned from the organisation's first year of service are concluded.
Abstract: Free access to a signals archive and a signal processing/analysis software library fosters online collaboration. This article aims to introduce PhysioNet as a resource to the biomedical research community. After a capsule summary of its history and goals, we discuss what PhysioNet offers to researchers, describe some of the technology needed to support these functions, and conclude with observations gleaned from PhysioNet's first year of service.

331 citations


"The impact of the MIT-BIH Arrhythmi..." refers background in this paper

  • ...org/) , a web-based resource for research on complex physiologic signals [5]....

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Proceedings ArticleDOI
23 Sep 1990
TL;DR: The European project for the development of an ST-T annotated database originated from the 'Concerted Action' on ambulatory monitoring, set up by the European Community in 1985 and includes more than 200 ST segment and almost 300 T-wave changes.
Abstract: The European project for the development of an ST-T annotated database originated from the 'Concerted Action' on ambulatory monitoring, set up by the European Community in 1985. The goal was to define an electrocardiographic (ECG) database for assessing the quality of ambulatory ECG monitoring (AECG) systems. Thirteen research groups from 8 countries continued providing AEGG tapes and annotating beat-by-beat the selected 2-channel records, each 2 hours in duration. ST segment and T-wave changes were identified and their onset, offset and peak beats annotated in addition to QRSs, beat types, rhythm and signal quality changes. The first set of 50 records was completed and stored on compact-disk ROM. It includes more than 200 ST segment and almost 300 T-wave changes. In cooperation with the developers of the Massachusetts Institute of Technology (MIT)-Boston's Beth Israel Hospital (BIH) arrhythmia database, the annotation scheme was devised to be consistent with both MIT-BIH and American Heart Association (AHA) formats. >

64 citations


"The impact of the MIT-BIH Arrhythmi..." refers background or methods in this paper

  • ...Other Long-Term ECG Databases No discussion of the MIT-BIH Arrhythmia Database would be complete without mention of the two other important collections of long-term ECGs that are also available to researchers: the AHA Database for Evaluation of Ventricular Arrhythmia Detectors [12] and the European ST-T Database [13]....

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  • ...The first 50 records of the European ST-T Database were completed and made available to researchers in 1990 [13], and the remainder of the database was completed in 1991....

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  • ...No discussion of the MIT-BIH Arrhythmia Database would be complete without mention of the two other important collections of long-term ECGs that are also available to researchers: the AHA Database for Evaluation of Ventricular Arrhythmia Detectors [12] and the European ST-T Database [13]....

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Proceedings ArticleDOI
13 Sep 1998
TL;DR: 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.
Abstract: 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.

15 citations


"The impact of the MIT-BIH Arrhythmi..." refers background in this paper

  • ...rations between geographically scattered researchers, as in an ongoing project to develop a long-term ST database of 24-hour recordings [14]....

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