2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-7.3
Paper Title AUTOMATIC ORDER SELECTION IN AUTOREGRESSIVE MODELING WITH APPLICATION IN EEG SLEEP-STAGE CLASSIFICATION
Authors Farah Nassif, Soosan Beheshti, Ryerson University, Canada
SessionSPTM-7: Estimation Theory and Methods 1
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Signal Processing Theory and Methods: [SSP] Statistical Signal Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper investigates the order selection problem for autoregressive models from a new perspective. It is known that the modeling error is a decreasing function of the model complexity and cannot directly be used for order selection. In the proposed approach, denoted by minimum mismatch modeling error (3ME), the modeling error is used to estimate the 3ME which is the true representation of the optimum order. The proposed approach provides probabilistic upperbounds on the mismatch modeling error using a statistical learning approach. Simulation results on generated synthetic data shows advantages of the 3ME method compared to existing order selection methods such as AIC and BIC as it avoids model overparametrizing or underparametrizing and improves the accuracy. 3ME can automate AR order selection which is a valuable feature. As shown in the simulation results for sleep-stage classification, the automated estimated order can be used as an additional feature in the classification process to increase accuracy.