Development of a real-time QRS beat classifier using a nonlinear trimmed moving averaging-based system

Szi Wen Chen; H. C. Chen

doi:10.1109/cic.2003.1291221

Development of a real-time QRS beat classifier using a nonlinear trimmed moving averaging-based system

Szi Wen Chen^*, H. C. Chen

^*Corresponding author for this work

Department of Electronic Engineering

Chang Gung University

Research output: Contribution to journal › Conference article › peer-review

5 Scopus citations

Abstract

In this paper, a real-time QRS beat classification system devised based on a nonlinear trimmed moving averaging filter is presented. Such a nonlinear system aims to identify the abnormal beat of ventricular origin from the normal one. The proposed beat classifier can function in parallel with a real-time QRS detector, permitting the tasks of beat detection and diagnosis to alternate with each other. Algorithm performance was evaluated against the ECG recordings selected from the MIT-BIH arrhythmia database. Numerical results demonstrated that over 99.8% beat identification rate can be achieved by the algorithm.

Original language	English
Pages (from-to)	577-580
Number of pages	4
Journal	Computers in Cardiology
Volume	30
DOIs	https://doi.org/10.1109/cic.2003.1291221
State	Published - 2003
Event	Computers in Cardiology 2003 - Thessaloniki Chalkidiki, Greece Duration: 21 09 2003 → 24 09 2003

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abstract = "In this paper, a real-time QRS beat classification system devised based on a nonlinear trimmed moving averaging filter is presented. Such a nonlinear system aims to identify the abnormal beat of ventricular origin from the normal one. The proposed beat classifier can function in parallel with a real-time QRS detector, permitting the tasks of beat detection and diagnosis to alternate with each other. Algorithm performance was evaluated against the ECG recordings selected from the MIT-BIH arrhythmia database. Numerical results demonstrated that over 99.8% beat identification rate can be achieved by the algorithm.",

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AB - In this paper, a real-time QRS beat classification system devised based on a nonlinear trimmed moving averaging filter is presented. Such a nonlinear system aims to identify the abnormal beat of ventricular origin from the normal one. The proposed beat classifier can function in parallel with a real-time QRS detector, permitting the tasks of beat detection and diagnosis to alternate with each other. Algorithm performance was evaluated against the ECG recordings selected from the MIT-BIH arrhythmia database. Numerical results demonstrated that over 99.8% beat identification rate can be achieved by the algorithm.

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JF - Computers in Cardiology

T2 - Computers in Cardiology 2003

Y2 - 21 September 2003 through 24 September 2003

ER -

Development of a real-time QRS beat classifier using a nonlinear trimmed moving averaging-based system

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