Survey of computer-assisted electrocardiography in the United States

Drazen, E; Mann, Nancy K.; Borun, Raymond; Laks, Michael M.; Bersen, Alan

doi:10.1016/0022-0736(88)90068-4

Cited by 35 publications

(13 citation statements)

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“…Automated ECG beat classification was traditionally performed using a decision-tree-like approach, based on various features extracted from an ECG beat [1], [4], [5], [13], [20], [22]. The features used include the width and height of QRS complex, RR interval, QRS complex area, etc.…”

Section: A Ecg Beat Classification Techniquesmentioning

confidence: 99%

A patient-adaptable ECG beat classifier using a mixture of experts approach

Palreddy

Tompkins

1997

IEEE Trans. Biomed. Eng.

492

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Abstract-We present a "mixture-of-experts" (MOE) approach to develop customized electrocardigram (ECG) beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. A small customized classifier is developed based on brief, patient-specific ECG data. It is then combined with a global classifier, which is tuned to a large ECG database of many patients, to form a MOE classifier structure. Tested with MIT/BIH arrhythmia database, we observe significant performance enhancement using this approach.Index Terms-ECG beat classification, MIT/BIH database, mixture of experts, neural network, patient adaptation.

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Section: A Ecg Beat Classification Techniquesmentioning

confidence: 99%

A patient-adaptable ECG beat classifier using a mixture of experts approach

Palreddy

Tompkins

1997

IEEE Trans. Biomed. Eng.

492

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“…The Electrocardiogram remains the most commonly performed cardiac test, which is essential to the diagnosis and management of a large number of cardiac pathologies. Given the large number of ECG acquired daily, computerized interpretation of tracings can be extremely time-saving and conducive to clinical decision support [1]. For this reason, automated computer programs have nowadays become a fixed feature in the great majority of commercially available ECG devices.…”

Section: Introductionmentioning

confidence: 99%

Self-organizing visualization and pattern matching of vectorcardiographic QRS waveforms

Yang

Leonelli

2016

Computers in Biology and Medicine

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QRS morphology is commonly used in the electrocardiographic diagnosis of ventricular depolarization such as left bundle branch block (LBBB) and ventricular septal infarction. We investigated whether pattern matching of QRS loops in the 3-dimensional vectorcardiogram (VCG) will improve the grouping of patients whose space-time electrical activity akin to each other, thereby assisting in clinical decision making. First, pattern dissimilarity of VCG QRS loops is qualitatively measured and characterized among patients, resulting in a 93×93 distance matrix of patient-to-patient dissimilarity. Each patient is then represented as a node in the network (or a star in the galaxy), but node locations are optimized to preserve the dissimilarity matrix. The optimization is achieved with a self-organizing algorithm that iteratively minimizes the network energy. Experimental results showed that patients' locations converge as the representation error reaches a stable phase. The convergence is independent of initial locations of network nodes. Most importantly, 93 patients are automatically organized into 3 clusters of healthy control, LBBB, and infarction. Spatial coordinates of nodes (or patients) are evidently novel predictors that can be used in the computer-assisted detection of cardiac disorders. Self-organizing pattern matching is shown to have strong potentials for large-scale unsupervised learning of patient groups.

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“…In a recent survey into the use of computers for analysis of resting ECGs, Drazen [1] suggested that over 50 million ECGs were reported using computer techniques in North America in 1987. The level of projected sales suggests that this total will have at least doubled by 1990.…”

Section: Introductionmentioning

confidence: 99%

A Brief History of Computer-Assisted Electrocardiography

1990

Methods Inf Med

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Survey of computer-assisted electrocardiography in the United States

Cited by 35 publications

References 1 publication

A patient-adaptable ECG beat classifier using a mixture of experts approach

A patient-adaptable ECG beat classifier using a mixture of experts approach

Self-organizing visualization and pattern matching of vectorcardiographic QRS waveforms

A Brief History of Computer-Assisted Electrocardiography

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