Wavelet classification of high frequency pupillary responses

Shi, Bin; Moloney, Kevin P.; Pan, Ying; Leonard, V. Kathlene; Vidaković, Brani; Jacko, Julie A.; Sainfort, François

doi:10.1080/10629360500107873

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…There are many ways to define the broadness ( B ). In this paper, we select the following definition [ 22 ].…”

Section: Scaling Wavelets and Multifractal Spectrummentioning

confidence: 99%

Analysis and classification of 1H-NMR spectra by multifractal analysis

Kim¹,

Woo²,

Vimalajeewa³

et al. 2023

PLoS ONE

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The objective of this research focuses on the development of a statistical methodology able to answer the question of whether variation in the intake of sulfur amino acids (SAA) affects the metabolic process. Traditional approaches, which evaluate specific biomarkers after a series of preprocessing procedures, have been criticized as not being fully informative, as well as inappropriate for translation of methodology. Rather than focusing on particular biomarkers, our proposed methodology involves the multifractal analysis that measures the inhomogeneity of regularity of the proton nuclear magnetic resonance (1H-NMR) spectrum by wavelet-based multifractal spectrum. With two different statistical models (Model-I and Model-II), three different geometric features of the multifractal spectrum of each 1H-NMR spectrum (spectral mode, left slope, and broadness) are employed to evaluate the effect of SAA and discriminate 1H-NMR spectra associated with different treatments. The investigated effects of SAA include group effect (high and low doses of SAA), depletion/repletion effect, and time over data effect. The 1H-NMR spectra analysis outcomes show that group effect is significant for both models. The hourly variation in time and depletion/repletion effects does not show noticeable differences for the three features in Model-I. However, these two effects are significant for the spectral mode feature in Model-II. The 1H-NMR spectra of the SAA low groups exhibit highly regular patterns with more variability than that of the SAA high groups for both models. Moreover, the discriminatory analysis conducted using the support vector machine and the principal components analysis shows that the 1H-NMR spectra of SAA high and low groups can be easily discriminatory for both models, while the spectra of depletion and repletion within these groups are discriminatory for Model-I and Model-II. Therefore, the study outcomes imply that the amount of SAA is important and that SAA intake affects mostly the hourly variation of the metabolic process and the difference between depletion and repletion each day. In conclusion, the proposed multifractal analysis of 1H-NMR spectra provides a novel tool to investigate metabolic processes.

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“…There are many ways to define the broadness ( B ). In this paper, we select the following definition [ 22 ].…”

Section: Scaling Wavelets and Multifractal Spectrummentioning

confidence: 99%

Analysis and classification of 1H-NMR spectra by multifractal analysis

Kim¹,

Woo²,

Vimalajeewa³

et al. 2023

PLoS ONE

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“…In this way, an adaptive system could take more or less responsibility depending upon cognitive workload levels gleaned from a user's PRB. While automated or predictive classification was not the particular focus of this study, we are currently working to build predictive models of user classification, based on these multifractal spectrum summary measures, as well as other multiscale measures [Shi et al 2005a[Shi et al , 2005b.…”

Section: Recent Work Bymentioning

confidence: 99%

Leveraging data complexity

Moloney

Jacko

Vidaković

et al. 2006

ACM Trans. Comput.-Hum. Interact.

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The current ubiquity of information technology has increased variability among users, creating a corresponding need to properly capture and understand these individual differences. This study introduces a novel application of multifractal statistical methods to distinguish users via patterns of variability within high frequency pupillary response behavior (PRB) data collected during computer-based interaction. PRB was measured from older adults, including two groups diagnosed with age-related macular degeneration (AMD) maintaining a range of visual acuities (n = 14), and one visually healthy control group (i.e., disease-free, 20/20-20/32 acuity) (n = 14). Three measures of the multifractal spectrum, the distribution of regularity indices extracted from time series data, distinguished the user groups, including: 1) spectral mode; 2) broadness; and 3) left slope. The results demonstrate a clear relationship between the values of these measures and the level of visual capabilities. These analytical techniques leverage the inherent complexity and richness of this high frequency physiological response data, which can be used to meaningfully differentiate individuals whose sensory and cognitive capabilities may be affected by aging and visual impairment. Multifractality analysis provides an objective, quantifiable means of uncovering and examining the underlying signatures in physiological behavior that may account for individual differences in interaction needs and behaviors.

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“…For example, wavelets are used to denoise EEG signals for detecting ADHD-affected children in Lee et al 10 . Similarly, a wavelet-based method is used in Shi et al 11 to improve classification performance of multiscale Schur monotone measures employed to analyze pupillary behaviors of individuals with ocular disease. However, to the best of our knowledge, no studies have explored self-similarity in pupillary dynamics.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD)

Vimalajeewa

McDonald

Bruce

et al. 2022

Sci Rep

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Attention deficit hyperactivity disorder (ADHD) is a common cognitive disorder affecting children. ADHD can interfere with educational, social, and emotional development, so early detection is essential for obtaining proper care. Standard ADHD diagnostic protocols rely heavily on subjective assessments of perceived behavior. An objective diagnostic measure would be a welcome development and potentially aid in accurately and efficiently diagnosing ADHD. Analysis of pupillary dynamics has been proposed as a promising alternative method of detecting affected individuals effectively. This study proposes a method based on the self-similarity of pupillary dynamics and assesses its strength as a potential diagnostic biomarker. Localized discriminatory features are developed in the wavelet domain and selected via a rolling window method to build classifiers. The application on a task-based pupil diameter time series dataset of children aged 10–12 years shows that the proposed method achieves greater than 78% accuracy in detecting ADHD. Comparing with a recent approach that constructs features in the original data domain, the proposed wavelet-based classifier achieves more accurate ADHD classification with fewer features. The findings suggest that the proposed diagnostic procedure involving interpretable wavelet-based self-similarity features of pupil diameter data can potentially aid in improving the efficacy of ADHD diagnosis.

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Wavelet classification of high frequency pupillary responses

Cited by 10 publications

References 9 publications

Analysis and classification of 1H-NMR spectra by multifractal analysis

Analysis and classification of 1H-NMR spectra by multifractal analysis

Leveraging data complexity

Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD)

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