ABACUS: a knowledge-based system for the interpretation of arrhythmias in long term ECG

Taddei, Alessandro; Niccolai, M.; Emdin, Michele; Marchesi, Carlo

doi:10.1109/cic.1993.378297

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The HFA is at each of the states in only with a specific degree of certainty or fuzziness . In the process of recognition, an HFA moves from the fuzzy source states in , to the fuzzy destination states in , with each input as in (3). At each transition, the membership of each destination state is updated based on the current membership of the source and destination state, the membership of the current input, and the transition membership.…”

Section: Hfasmentioning

confidence: 99%

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A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

Tümer

Belfore

Ropella

2003

IEEE Trans. Syst., Man, Cybern. B

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Section: Hfasmentioning

confidence: 99%

“…The first approach is that of the knowledge-based diagnostic tools [1]- [3]. A diagnostic tool based on this approach has a knowledge base and a rule base.…”

mentioning

confidence: 99%

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

Tümer

Belfore

Ropella

2003

IEEE Trans. Syst., Man, Cybern. B

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“…The knowledge-base can be set up in consultation with the experts in the field of cardiology[99]. For example, Taddei et al developed a knowledge-based system for interpretation of arrhythmias in long-term ECG[100].…”

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confidence: 99%

Kernel machines and classifier ensemble learning for biomedical applications

Li¹

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A Granular Description of ECG Signals

Gacek

Pedrycz²

2006

IEEE Trans. Biomed. Eng.

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In this paper, we develop a general framework of a granular representation of ECG signals. The crux of the approach lies in the development and ongoing processing realized in the setting of information granules-fuzzy sets. They serve as basic conceptual and semantically meaningful entities using which we describe signals and build their models (such as various predictive schemes or classifiers). A comprehensive two-phase scheme of the design of the information granules is proposed and described. At the first phase, we discuss the temporal granulation through a series of temporal windows (granular windows) and an aggregation of the values of signal by means of fuzzy sets. To address this issue, offered is a detailed method of building a fuzzy set based on numeric data and a certain optimization criterion that strikes a balance between the highest experimental relevance of the fuzzy set supported by numeric data and its substantial specificity. At the next phase of the granular design, a collection of information granules is further summarized with the use of fuzzy clustering (Fuzzy C-Means). The resulting prototypes (centroids) formed by this grouping process serve as elements of the granular vocabulary. We discuss ways of using these vocabularies in the knowledge-based representation, modeling, and classification of ECG beats.

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ABACUS: a knowledge-based system for the interpretation of arrhythmias in long term ECG

Cited by 3 publications

References 7 publications

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

Kernel machines and classifier ensemble learning for biomedical applications

A Granular Description of ECG Signals

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