MVME-RCMFDE framework for discerning hyper-responsivity in Autism Spectrum Disorders

Autism Spectrum Disorder (ASD), characterized by impaired sensory processing, has a wide range of clinical heterogeneity, which handicaps effective therapeutic interventions. Therefore, it is imperative to develop potential mechanisms for delineating clinically meaningful subgroups, so as to provide...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computers in biology and medicine Ročník 149; s. 105958
Hlavní autori: Sheela, Priyalakshmi, Puthankattil, Subha D.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.10.2022
Elsevier Limited
Predmet:
Adaptive chirp mode decomposition
Algorithms
Autism
Automation
Biomarkers
Complexity
Constituents
Electroencephalography
Empirical mode decomposition
Entropy
Frequencies
Heterogeneity
Information processing
Internal Medicine
Medical treatment
Other
Pattern analysis
Refined composite multiscale fluctuation-based dispersion entropy
Sensory integration
Sensory perception
Subgroups
Therapeutic applications
Theta rhythms
Time series
Time varying filter-empirical mode decomposition
Variational mode extraction
Visual evoked potentials
Wavelet transforms
Refined composite multiscale fluctuation-based dispersion entropy
Visual evoked potentials
Empirical mode decomposition
Variational mode extraction
Adaptive chirp mode decomposition
Time varying filter-empirical mode decomposition
ISSN:0010-4825, 1879-0534, 1879-0534
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Autism Spectrum Disorder (ASD), characterized by impaired sensory processing, has a wide range of clinical heterogeneity, which handicaps effective therapeutic interventions. Therefore, it is imperative to develop potential mechanisms for delineating clinically meaningful subgroups, so as to provide individualised medical treatment. In this study, an attempt is being made to differentiate the hyper-responsive subgroup from ASD by analysing the complexity pattern of Visual Evoked Potentials (VEPs), recorded from a group of 30 ASD participants, in the presence of vertical achromatic sinewave gratings at varying contrast conditions of low (5%), medium (50%) and high (90%). This study proposes a new diagnostic framework incorporating a novel signal decomposition method termed as Modified Variational Mode Extraction (MVME) and a multiscale entropy approach. MVME segments the signal into five constituent modes with less spectral overlap in lower frequencies. Refined Composite Multiscale Fluctuation-based Dispersion entropy (RCMFDE) is extracted from these constituent modes, thereby facilitating the identification of hyper-responsive subgroup in ASD. When tested on both simulated and real VEPs, MVME displays appreciable performance in terms of root mean square error and minimal spectral overlap in the lower frequencies, in comparison with the other state-of-the-art techniques. Relative Complexity analysis with RCMFDE exhibits a rising trend in 43%–50% of ASD in modes 1, 2, 3 and 4. The proposed MVME-RCMFDE approach is efficient in discriminating the hyper-responsive subgroup in ASD in multiple modes namely mode 1, 2, 3 and 4, which correspond to delta, theta, alpha and beta frequency bands of brain signals. •Altered sensory processing is a major diagnostic criterion for ASD.•Complexity pattern of VEPs is studied to discern hyper-responsive subgroup in ASD.•Proposed MVME method to decompose VEPs into constituent modes.•RCMFDE is employed as the complexity metric.•MVME-RCMFDE framework identifies hyper-responsivity in multiple frequency bands.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2022.105958