A comprehensiveassessment was presented for the determination of optimized feature vectors forECG signals and best-performing classifier for these optimized feature vectors wasdetermined.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study, a feature vector optimization based method has been proposed forclassification of the heartbeat types.
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Key Findings & Data:Firstly, wavelet transform (WT)method were applied on these ECG signals to generate all feature vectors.Optimizing these feature vectors is provided by performing particle swarmoptimization (PSO), genetic search, best first, greedy stepwise and multi objectiveevoluationary algorithms on these vectors.
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Conclusion & Impact:A comprehensiveassessment was presented for the determination of optimized feature vectors forECG signals and best-performing classifier for these optimized feature vectors wasdetermined.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this study, a feature vector optimization based method has been proposed forclassification of the heartbeat types. Electrocardiogram (ECG) signals of fivedifferent heartbeat type were used for this aim. Firstly, wavelet transform (WT)method were applied on these ECG signals to generate all feature vectors.Optimizing these feature vectors is provided by performing particle swarmoptimization (PSO), genetic search, best first, greedy stepwise and multi objectiveevoluationary algorithms on these vectors. These optimized feature vectors arelater applied to the classifier inputs for performance evaluation. A comprehensiveassessment was presented for the determination of optimized feature vectors forECG signals and best-performing classifier for these optimized feature vectors wasdetermined.
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Cite This Article
YILDIRIM, Ö., BALOĞLU, U. B. (2022). Heartbeat type classification with optimized feature vectors. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 8 - Sayı: 2, 170–175. https://ajindex.com/articles/440651
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