Title :
An adaptive RBF neural network model for evoked potential estimation
Author :
Fung, Kenneth S M ; Chan, Francis H Y ; Lam, F.K. ; Poon, Paul W F
Author_Institution :
Dept. of Electr. & Electron. Eng., Hong Kong Univ., Hong Kong
fDate :
30 Oct-2 Nov 1997
Abstract :
A method for evoked potential estimation based on an adaptive radial basis function neural network (RBFNN) model is presented in this paper. During training, the number of hidden nodes (number of RBFs) and model parameters are adjusted to fit the target signal which is obtained by averaging. In order to reduce computational complexity and the influence of noise in estimating single-trial evoked potential (EP), the number of hidden nodes is also minimized in training. After training, both peak latency and amplitude, being distinctive features of an EP, are characterized by center and height of the corresponding RBF respectively. In EP estimation, an adaptive algorithm is employed to track the peaks from trial to trial by adapting the center and height of RBFs directly. The adaptive RBFNN is tested on a computer simulated data set and clinical EP recording. Our proposed algorithm is suitable for tracking EP waveform variations
Keywords :
adaptive estimation; adaptive signal processing; auditory evoked potentials; bioelectric potentials; computational complexity; learning (artificial intelligence); medical signal processing; pattern classification; radial basis function networks; visual evoked potentials; adaptive algorithm; adaptive radial basis function neural network model; averaging; brainstem auditory evoked potential; computational complexity; evoked potential estimation; model parameters; network optimization algorithm; number of hidden nodes; peak amplitude; peak latency; single-trial evoked potential; training; virtual peak; visual evoked potential; waveform variations tracking; Adaptive algorithm; Adaptive systems; Computational complexity; Computational modeling; Computer simulation; Delay; Neural networks; Noise reduction; Radial basis function networks; Testing;
Conference_Titel :
Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-4262-3
DOI :
10.1109/IEMBS.1997.756542
