Title :
Adaptive signal enhancement of somatosensory evoked potentials based on least mean squares and Kalman filter: A comparative study
Author :
Zhao, H.S. ; Zhang, Z.G. ; Liu, H.T. ; Luk, K.D.K. ; Hu, Y.
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
fDate :
April 29 2009-May 2 2009
Abstract :
This paper undertakes a comparative study of adaptive signal enhancers (ASE) of somatosensory evoked potentials (SEP) for spinal cord compression detection. We compare the ASE methods based on two adaptive filtering algorithms: the least mean squares (LMS) and Kalman filter (KF) in terms of their convergence rate, variability, and complexity. In addition, the two ASE methods are compared with the conventional ensemble averaging (EA) method for SEP extraction. Experimental results on a rat model show that the LMS-based and KF-based ASE methods have similar superior performance over the EA method and the two ASE methods also exhibit some slightly different properties during SEP extraction.
Keywords :
adaptive Kalman filters; adaptive signal detection; bioelectric potentials; least mean squares methods; mechanoception; medical signal detection; neurophysiology; Kalman filter; adaptive signal enhancement; complexity; conventional ensemble averaging; convergence rate; least mean squares; rat model; somatosensory evoked potentials; spinal cord compression detection; two-adaptive filtering algorithms; variability; Adaptive filters; Convergence; Filtering algorithms; Instruments; Least squares approximation; Monitoring; Plasma welding; Signal processing algorithms; Spinal cord; Surgery; Kalman filter; adaptive signal enhancement; ensemble averaging; least mean squares; somatosensory evoked potential;
Conference_Titel :
Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on
Conference_Location :
Antalya
Print_ISBN :
978-1-4244-2072-8
Electronic_ISBN :
978-1-4244-2073-5
DOI :
10.1109/NER.2009.5109402
