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Feature extraction from optimal time-frequency and time-scale transforms for the classification of the knee joint vibroarthrographic signals
, Article 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003, 14 December 2003 through 17 December 2003 ; 2003 , Pages 709-712 ; 0780382927 (ISBN); 9780780382923 (ISBN) ; Shamsollahi, M. B ; Rahimi, A ; Behzad, M ; Afkari, P ; Zamani, E. A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2003
Abstract
In this study knee joint vibroarthrographic (VAG) signals are recorded during active knee movements, which are essentially non-stationary. Because of this nature, common frequency methods are unable to represent the signals, accurately. Both time-frequency and time-scale transforms are used in this research which are good tools for studying non-stationary signals. By optimizing the utilized transforms, it was concluded that the wavelet packet, having the ability of multiresolutional analysis, is a more promising method to extract features from the VAG signals. The performance of different feature extraction techniques were compared by using three new recorded and extensive databases,...
ECG beat classification based on a cross-distance analysis
, Article 6th International Symposium on Signal Processing and Its Applications, ISSPA 2001, Kuala Lumpur, 13 August 2001 through 16 August 2001 ; Volume 1 , 2001 , Pages 234-237 ; 0780367030 (ISBN); 9780780367036 (ISBN) ; Nayebi, K ; Sharif University of Technology
IEEE Computer Society
2001
Abstract
This paper presents a multi-stage algorithm for QRS complex classification into normal and abnormal categories using an unsupervised sequential beat clustering and a cross-distance analysis algorithm. After the sequential beat clustering, a search algorithm based on relative similarity of created classes is used to detect the main normal class. Then other classes are labeled based on a distance measurement from the main normal class. Evaluated results on the MIT-BIH ECG database exhibits an error rate less than 1% for normal and abnormal discrimination and 0.2% for clustering of 15 types of arrhythmia existing on the MIT-BIH database. © 2001 IEEE