Wavelet-Based Robust Estimation of Hurst Exponent with Application in Visual Impairment Classification

Journal of Data Science

article

Chen Feng¹  Yajun Mei¹  Brani Vidakovic² 

[1] H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology;Department of Statistics, Texas A&M University, College Station

关键词: pupillary response behavior;    high-frequency data;    non-decimated wavelet transforms;   

DOI  :  10.6339/JDS.202010_18(4).0001

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Pupillary response behavior (PRB) refers to changes in pupil diameter in response to simple or complex stimuli. There are underlying, unique patterns hidden within complex, high-frequency PRB data that can be utilized to classify visual impairment, but those patterns cannot be described by traditional summary statistics. For those complex high-frequency data, Hurst exponent, as a measure of long-term memory of time series, becomes a powerful tool to detect the muted or irregular change patterns. In this paper, we proposed robust estimators of Hurst exponent based on non-decimated wavelet transforms. The properties of the proposed estimators were studied both theoretically and numerically. We applied our methods to PRB data to extract the Hurst exponent and then used it as a predictor to classify individuals with different degrees of visual impairment. Compared with other standard wavelet-based methods, our methods reduce the variance of the estimators and increase the classification accuracy.

【 授权许可】

CC BY