Intelligent systems and applications in engineering advanced technology and science
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Dosyalar
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Feature extraction that is detection of effective features is one of the phases of biomedical signal classification. In feature extraction phase, the detection of features that increase performance of classification is very important in terms of diagnosis of disease. Due to this reason, the using of an effective algorithm for feature extraction increases classification accuracy and also it decreases processing time of classifier. In this study, two well-known dictionary-learning algorithms are used to extract features of ECG signals. The features of ECG signals are extracted by using Method of Optimal Direction (MOD) and K-Singular Value Decomposition (K-SVD). However, the extracted features are classified by Artificial Neural Network (ANN). Twelve different ECG signal classes which taken from MIT-BIH ECG Arrhythmia Database are used. When the obtained results are examined, it is seen that performance of classifier increases in usage of K-SVD for feature extraction. The highest classification accuracy is obtained as 98.74% with 5 nonzero elements in [20 1] feature vector, while K-SVD is used in feature extraction phase. The obtained results are assessed by comparing with the results obtained when discrete wavelet transform and principal component analysis are used
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yapay Zeka, ECG Classification, Method of Optimal Direction, Value Decomposition
Kaynak
International Journal of Intelligent Systems and Applications in Engineering
WoS Q Değeri
Scopus Q Değeri
Cilt
6
Sayı
1
Künye
Ceylan, R. (2018). Intelligent Systems and Applications in Engineering Advanced Technology and Science. International Journal of Intelligent Systems and Applications in Engineering, 6(1), 40-46.
