Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine

DOI10.1016/J.AMC.2006.08.020zbMath1112.92033OpenAlexW2025980594MaRDI QIDQ876679

Kemal Polat, Salih Güneş

Publication date: 26 April 2007

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2006.08.020

zbMATH Keywords

ROC curvesleast squares support vector machine (LSSVM)

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Medical applications (general) (92C50) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (5)

Multiclass maximum margin clustering via immune evolutionary algorithm for automatic diagnosis of electrocardiogram arrhythmias ⋮ PMSVM: an optimized support vector machine classification algorithm based on PCA and multilevel grid search methods ⋮ A novel automatic detection system for ECG arrhythmias using maximum margin clustering with immune evolutionary algorithm ⋮ Multi-class support vector machine optimized by inter-cluster distance and self-adaptive differential evolution ⋮ A patient adaptable ECG beat classifier based on neural networks

Uses Software

• UCI-ml

Cites Work

• Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition.
• Chaos control using least-squares support vector machines
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