Case based time series prediction using biased time warp distance for electrical evoked potential forecasting in visual prostheses

  • Authors:

  • Jin Qi;Jie Hu;Yinghong Peng;Xinyu Chai;Qiushi Ren

  • 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;Department of Biomedical Engineering, Peking University, 60 Yannan Yuan, Beijing 100871, China

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2013

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Abstract

Case based time series prediction (CTSP) is a machine learning technique to predict the future behavior of the current time series by referring similar old cases. To reduce the cost of the visual prostheses research, we devote to the investigation of predictive performance of CTSP in electrical evoked potential (EEP) prediction instead of doing numerous biological experiments. The heart of CTSP for EEP prediction is a similarity measure of training case for target electrical stimulus by using distance metric. As EEP experimental case consists of the stationary electrical stimulation values and time-varying EEP elicited values, this paper proposes a new distance metric which takes the advantage of point-to-point distance's efficient operation in stationary data and time series distance's high capability in temporal data, called as biased time warp distance (BTWD). In BTWD metric, stimulation set difference (Diff_I) and EEP sequence difference (Diff_II) are calculated respectively, and a time-dependent bias configuration is added to reflect the different influences of Diff_I and Diff_II to the numerical computation of BTWD. Similarity-related adaptation coefficient summation is employed to yield the predictive EEP values at given time point in principle of k nearest neighbors. The proposed predictor using BTWD was empirically tested with data collected from the electrophysiological EEP eliciting experiments. We statistically validated our results by comparing them with other predictor using classical point-to-point distances and time series distances. The empirical results indicated that our proposed method produces superior performance in EEP prediction in terms of predictive accuracy and computational complexity.