Amin Janghorbani - Amirkabir University of Technology Tehran, Iran
Abdollah Arasteh - Amirkabir University of Technology Tehran, Iran
Mohammad Hassan Moradi - Amirkabir University of Technology Tehran, Iran

predicting the future behavior of human's biosignals can help clinicians to prevent occurrence of physiological disorders such as hypotension, hypertension, epilepsy, etc. In addition this prediction helps clinicians to buy some time in order to select a more effective treatment for physiological disorders without exposing the patient to additional risks of delay in receiving treatment. In this paper a local linear neuro-fuzzy model was applied to predict mean arterial pressure time series. In order to evaluate the accuracy of prediction, Normalized Mean Square Error (NMSE) was chosen as an error index. 10 mean arterial pressure signals (2.5 hours each) from 10 patients were selected for training and prediction. Mean of NMSE for these signals was 0.023 in train and 0.0514 in test.

Time Series, Prediction, Local Linear Model, Neuro-Fuzzy, Local Linear Model Tree (LoLiMoT) algorithm