Title :
Classification of time-varying signals using time-frequency atoms
Author :
Wellig, Peter ; Moschytz, George S.
Author_Institution :
Signal & Inf. Process. Lab., Swiss Fed. Inst. of Technol., Zurich, Switzerland
Abstract :
Extracting relevant features from signals is a key element in classification of signals, e.g., for the decomposition of electromyograms (EMG signals). We present an algorithm which uses time-frequency dictionaries and adaptively selects a small number of discriminant time-frequency atoms. Using our method, simulations show reduced misclassification rates compared to commonly-used linear classifiers
Keywords :
digital simulation; discrete wavelet transforms; electromyography; medical signal processing; signal classification; time-frequency analysis; EMG decomposition algorithms; EMG segments; class separability; classification; discriminant quality function; discriminant time-frequency atoms; discriminant wavelet packet atoms; electromyogram signals; local discriminant basis algorithm; misclassification rates; simulations; time-frequency dictionaries; time-varying signals; wavelet packet atoms; Data mining; Dictionaries; Electromyography; Electronic mail; Feature extraction; Information processing; Linear discriminant analysis; Signal processing; Time frequency analysis; Wavelet packets;
Conference_Titel :
[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-5674-8
DOI :
10.1109/IEMBS.1999.804107
