An initiative to classify different neurological disorder in children using multichannel EEG signals

Begum, Dilshad; Ravikumar, K. M.; Vykuntaraju, K. N.

doi:10.1109/rteict.2016.7808095

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…While many studies demonstrate change in MSE across development (Bosl et al, 2011;De Wel et al, 2017;Hasegawa et al, 2018;Kang et al, 2019;Lippé et al, 2009;McIntosh et al, 2008;Miskovic et al, 2016;Polizzotto et al, 2016;Szostakiwskyj et al, 2017;Zhang et al, 2009) or with developmental disorder (Begum et al, 2017;Chenxi et al, 2016;Eroğlu et al, 2020;Kang et al, 2018;Liu et al, 2017;Okazaki et al, 2015;Rezaeezadeh et al, 2020;Simon et al, 2017;Wadhera and Kakkar, 2020;Weng et al, 2017), the vastly differing preprocessing choices and widespread failure to adopt the critical MSE algorithm modification of scale-wise recalculation of the similarity criterion makes it challenging to compare results across studies and realize the role of entropy in neurodevelopment.…”

Section: Mse Across Development: An Overview Of Prior Pediatric Eeg Researchmentioning

confidence: 99%

The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

Puglia

Slobin

Williams

2021

Preprint

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It is increasingly understood that moment-to-moment brain signal variability – traditionally modeled out of analyses as mere “noise” – serves a valuable function role and captures properties of brain function related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) – a measure of signal irregularity across temporal scales – is an increasingly popular analytic technique in human neuroscience. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain’s moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in data preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized EEG preprocessing and entropy estimation pipeline that generates scale-wise entropy estimates that are reliable and capable of differentiating developmental stages and cognitive states. This novel pipeline – the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED. The dataset used herein to develop and validate the pipeline is available for download from https://openneuro.org/datasets/ds003710.

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Section: Mse Across Development: An Overview Of Prior Pediatric Eeg Researchmentioning

confidence: 99%

The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

Puglia

Slobin

Williams

2021

Preprint

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Criminal psychological emotion recognition based on deep learning and EEG signals

Liu

2020

Neural Comput & Applic

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RETRACTED ARTICLE: Recognition of autism in children via electroencephalogram behaviour using particle swarm optimization based ANFIS classifier

Kumar

Mohanalin²,

Mahil

2018

Multimed Tools Appl

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An initiative to classify different neurological disorder in children using multichannel EEG signals

Cited by 5 publications

References 14 publications

The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

Criminal psychological emotion recognition based on deep learning and EEG signals

RETRACTED ARTICLE: Recognition of autism in children via electroencephalogram behaviour using particle swarm optimization based ANFIS classifier

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