Title :
Next-generation decomposition of multi-channel EMG signals
Author :
Nawab, S.H. ; Wotiz, R.P. ; Hochstein, L.M. ; De Luca, C.J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Boston Univ., MA, USA
Abstract :
We have developed a knowledge-based system for the improved decomposition of multi-channel EMG signals. This system incorporates streamlined and/or modified versions of the basic algorithms in the precision decomposition technique. In addition, it employs the IPUS framework from artificial intelligence to implement signal re-processing strategies for the detection and subsequent correction of decomposition errors arising from its initial signal processing stage. Experiments on real EMG data indicate that our new system has significant speed as well as accuracy advantages over previous generations of precision decomposition programs.
Keywords :
electromyography; knowledge based systems; medical signal processing; artificial intelligence; decomposition errors correction; electrodiagnostics; initial signal processing stage; multichannel EMG signals; next-generation decomposition; precision decomposition programs; quasiperiodic pulse trains; signal reprocessing strategies; Artificial intelligence; Electrodes; Electromyography; Neuromuscular; Prototypes; Pulse measurements; Pulse shaping methods; Shape; Signal processing; Signal processing algorithms;
Conference_Titel :
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
Print_ISBN :
0-7803-7612-9
DOI :
10.1109/IEMBS.2002.1134375