EEG signal compression based on classified signature and envelope vector sets
Yükleniyor...
Dosyalar
Tarih
2009-03
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, a novel method to compress electroencephalogram (EEG) signal is proposed. The proposed method is based on the generation process of the classified signature and envelope vector sets (CSEVS), which employs an effective k-means clustering algorithm. It is assumed that both the transmitter and the receiver units have the same CSEVS. In this work, on a frame basis, EEG signals are modeled by multiplying only three factors called as classified signature vector, classified envelope vector, and gain coefficient (GC), respectively. In other words, every frame of an EEG signal is represented by two indices R and K of CSEVS and the GC. EEG signals are reconstructed frame by frame using these numbers in the receiver unit by employing the CSEVS. The proposed method is evaluated by using some evaluation metrics that are commonly used in this area such as root-mean-square error, percentage root-mean-square difference, and measuring with visual inspection. The performance of the proposed method is also compared with the other methods. It is observed that the proposed method achieves high compression ratios with low-level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.
Açıklama
Anahtar Kelimeler
EEG, Compression, Modeling, Wavelets, Clustering algorithms, Vectors, Visual communication, A-frames, Diagnostic informations, EEG signals, Evaluation metrics, Gain coefficients, Generation process, High compression ratios, K-means clustering algorithms, Novel methods, Percentage root-mean-square differences, Reconstruction errors, Root-mean-square errors, Signature vectors, Visual inspections, Electroencephalography
Kaynak
International Journal of Circuit Theory and Applications
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
37
Sayı
2
Künye
Gürkan, H., Güz, Ü. & Yarman, B. S. B. (2009). EEG signal compression based on classified signature and envelope vector sets. International Journal of Circuit Theory and Applications, 37(2), 351-363. doi:10.1002/cta.548
