Integration of similarity measurement and dynamic SVM for electrically evoked potentials prediction in visual prostheses research

  • Authors:

  • Jin Qi;Jie Hu;Yinghong Peng;Qiushi Ren;Weiming Wang;Zhenfei Zhan

  • Affiliations:

  • Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Laser Medicine and Bio-Photonics, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Electrical evoked potentials (EEPs) time series prediction is a novel topic concentrating on reducing the cost of the visual prostheses research. Support vector machine (SVM), a superior neural network algorithm, is a powerful tool for time series forecasting but is insensitive to multivariate analysis. Meanwhile, similarity measurement (SM), a key technology in case-based reasoning, has been applied in a wide variety of fields but is only limited to the point-to-point computation. This paper firstly attempts to take the advantages of SM and SVM to generate a high performance EEPs predictor. Four independent SM metrics, i.e. fuzzy SM, numeric SM, textual SM and interval SM are employed to calculate the similarities between input variables (including electrical stimulation parameter and spatial parameter) and corresponding experimental values. Then SVM is utilized to predict EEPs behavior in terms of the temporal input. Furthermore, we add the similarities and temporal weights into SVM to indicate that recent data from similar experimental cases could provide more information than distant data from dissimilar ones. Due to the dynamic property, the new SVM is called dynamic SVM, i.e. DSVM and the predictor is named SM-DSVM. How to implement the hybrid predictor with grid-search for parameter optimization is illustrated in detail. In the empirical comparison, the predictive performances on 30 hold-out data are used to make comparisons between SM-DSVM and other comparative predictors. Empirical results show that SM-DSVM is feasible and validated for EEPs prediction in visual prostheses research.